WO2017107010A1

WO2017107010A1 - Information analysis system and method based on event regression test

Info

Publication number: WO2017107010A1
Application number: PCT/CN2015/098086
Authority: WO
Inventors: 易峥; 夏炜; 陶志伟; 潘杭平
Original assignee: 浙江核新同花顺网络信息股份有限公司
Priority date: 2015-12-21
Filing date: 2015-12-21
Publication date: 2017-06-29

Abstract

One aspect of the present invention relates to an information analysis method based on an event regression test. The information analysis method can comprise: collecting information, pre-processing the information and extracting an entity in the information, processing the information and extracting a relevant attribute in the information, determining the type of information and generating a refinement event, generating a corresponding natural language sentence according to the generated refinement event, performing a regression test on the generated natural language sentence, and generating a regression testing report, etc. In addition, the present invention relates to a user being able to select any announcement or news to perform a regression test, and generate a regression testing report. Moreover, the present invention relates to a user being able to input a natural language sentence and perform a regression test on an input natural language sentence so as to generate a regression testing report. Furthermore, the present invention relates to an information analysis system based on an event regression test, comprising a collection module, a system database, a processing module, a natural language processing module and a regression testing module.

Description

Information analysis system and method based on event backtesting

Technical field

The invention relates to an information analysis system and method, in particular to automatically analyzing relevant information and natural language sentences obtained by an event, thereby obtaining historical backtesting information and the like.

Background technique

With the increasing popularity of the Internet, people are becoming more accustomed to using the Internet to obtain information or analyze information based on data. Due to the continuous expansion of Internet coverage and the continuous increase of information, data is also increasing. When people try to use the Internet to obtain certain information, they often encounter the characteristics of long information length and diverse information content, which takes time to read and analysis. At the same time, some industry sectors or users need to make historical decisions based on current information to make historical decisions. For example, in the financial industry, when using a backtest to evaluate the performance and effectiveness of the strategy over the past period of time, it is useful to help investors analyze investment decisions. In the weather forecast, according to the real-time data of temperature, humidity, and air pressure, it is possible to predict the future weather by analyzing historical weather conditions under the same conditions.

Brief

One aspect of the present invention is directed to an information analysis system including, according to one embodiment, a computer readable storage medium, the storage medium storing an executable module, the storage medium including a collection module, The collection module is capable of collecting information; the processing module is capable of pre-processing the collected information, extracting an event from the pre-processed information; and a natural language processing module capable of generating the generated event according to the extracted event A natural language statement; a backtest module capable of generating a backtest result according to the generated natural language statement combined with the history information. A processor capable of executing the executable module of the computer readable storage medium storage.

According to another embodiment of the present invention, the information analysis system further includes a a database capable of storing the collected information, pre-processed information, extracted events, natural language statements, historical information, and backtest results.

According to another embodiment of the present invention, the database includes an original information database, a text database, a text pre-processing database, an entity database, an event attribute database, a keyword database, a text classification database, a history information database, a natural language processing database, and event recognition. Database, backtest module database, text template database, dictionary database.

According to another embodiment of the present invention, the processing module of the information analysis system further includes a format conversion module, a text processing module, an attribute extraction module, and an event recognition module.

According to another embodiment of the invention, the processing module further comprises a text classification module.

According to another embodiment of the present invention, the method adopted by the processing module includes chi-square statistics, information gain, mutual information, odds ratio, cross entropy, inter-class information difference, keyword statistics, decision tree, Rocchio, Naïve Bayes Neural network, support vector machine, linear least squares fit, nearest neighbor algorithm, genetic algorithm, sentiment classification, maximum entropy, Generalized Instance Set, synonym configuration, Boolean association rules, position rules, machine learning.

According to another embodiment of the invention, the natural language processing module can receive information from the collection module.

According to another embodiment of the present invention, the backtesting module further includes backtesting information determination, and the backtesting information determines that the evaluation is given according to the condition of the backtesting result.

According to another embodiment of the invention, the backtest result can be presented to the user.

Another aspect of the present invention relates to an information analysis method including collecting information, extracting an event based on the information, generating a natural language sentence according to the event, and performing backtesting analysis on the natural language sentence.

According to another embodiment of the invention, the collection information includes user input information and non-user input information, the non-user input information source including a communication terminal and a server.

According to another embodiment of the present invention, the collected information includes announcement information and new Smell the information.

According to another embodiment of the invention, the extraction event further comprises entity identification and attribute extraction.

According to another embodiment of the invention, the entity identification further comprises format conversion, text segmentation, number and unit normalization processing.

According to another embodiment of the invention, the attribute extraction can be implemented by a system defined model.

According to another embodiment of the invention, the natural language statement may be generated based on the extraction event.

According to another embodiment of the invention, the natural language statement can be generated based on user input information.

According to another embodiment of the invention, the natural language statement is further expanded according to an event category.

According to another embodiment of the invention, the natural language sentence analysis includes backtesting the natural language statement.

According to another embodiment of the present invention, the natural language sentence backtesting may generate a backtest result according to the information category.

Description of the drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can apply the present invention to other similarities according to the drawings without any creative work. scene. Unless otherwise apparent from the language environment or otherwise stated, the same reference numerals in the drawings represent the same structure and operation.

Figure 1 is a schematic diagram of an exemplary system configuration of an information analysis system;

Figure 2 is a block diagram of the information analysis system;

Figure 3 shows the flow chart of information analysis;

Figure 4 is a schematic structural view of the collection module;

Figure 5 is a schematic structural view of a processing module;

FIG. 6 is a schematic structural diagram of a format conversion module;

Figure 7 is a schematic structural diagram of a text preprocessing module;

Figure 8 is a schematic structural diagram of a text classification module;

FIG. 9 is a schematic structural diagram of an attribute extraction module;

Figure 10 is a flow chart of the processing module;

Figure 11 is a schematic structural diagram of a natural language processing module;

Figure 12 is a schematic structural view of the back test module;

Figure 13 shows the backtest flow chart;

Figure 14 is a schematic structural diagram of a system database;

Figure 15 shows a flow chart of information analysis;

Figure 16 shows the online working flow chart of the information analysis system;

Figure 17 is a schematic diagram of an interactive interface of an information analysis system for news or announcements;

Figure 18 is a schematic diagram of an interactive interface of the information analysis system for user input;

Figure 19 shows the text of the announcement text used by the information analysis system.

specific description

The words "a", "an", "the" and "the" In general, the terms "comprising" and "comprising" are intended to include only the steps and elements that are specifically identified, and the steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

The information analysis method described in this specification refers to collecting information, processing information, generating natural language sentences, analyzing data to provide reference information, and the like. In some embodiments, an aspect of the invention relates to an information analysis system. The information analysis system can include a collection module, a system database, a processing module, a natural language processing module, and a backtest module. Another aspect of the invention relates to an information analysis method based on event backtesting. The information analysis method may include collecting information, preprocessing the information and extracting the entities in the information, processing the information and extracting related attributes in the information, determining the information category and generating a refinement event, according to the generation. The refinement event generates a corresponding natural language statement, backtests the generated natural language statement, generates a backtest report, and the like. Another aspect of the invention relates to the fact that the user can select any announcement or news to perform real-time backtesting and generate a backtest report. Another aspect of the invention relates to a user inputting any natural language statement and backtesting the input natural language statement to generate a backtest report in real time.

Different embodiments of the present invention are applicable to a variety of fields including, but not limited to, investments in finance and derivatives (including but not limited to stocks, bonds, gold, paper gold, silver, foreign exchange, precious metals, futures, money funds, etc.), Technology (including but not limited to mathematics, physics, chemistry and chemical engineering, biology and bioengineering, electrical engineering, communication systems, internet, internet of things, etc.), politics (including but not limited to politicians, political events, countries), news ( From the regional perspective, including but not limited to regional news, domestic news, international news; from the main body of the news, including but not limited to political news, sports news, science and technology news, economic news, life news, weather news, etc.). According to at least one embodiment of the present invention, various information resources, such as text, pictures, audio, and video content, can be quickly refined, and the backtesting strategy is generated according to relevant historical information, and a backtest report is generated to make the user more Quickly and easily understand the possible future impact of the information. Application scenarios of different embodiments of the present invention include, but are not limited to, one or more combinations of web pages, browser plug-ins, clients, customization systems, enterprise internal analysis systems, artificial intelligence robots, and the like. The above description of the applicable fields is merely a specific example and should not be considered as the only feasible implementation. Obviously, for those skilled in the art, after understanding the basic principles of an information analysis method and system based on event backtesting, it is possible to apply the above-mentioned methods and systems without departing from this principle. Various modifications and changes in form and detail, but such modifications and changes are still within the scope of the above description. For example, in one embodiment of the present invention, the backtest report is displayed to the user in a unified text form. For those skilled in the art, the back test report may also be displayed in a unified audio format or video format. user. Alternatives or modifications or variations similar to this are still within the scope of the invention. In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. Obviously, the drawings in the following description are only some embodiments of the present invention, and are not intended to be created by those skilled in the art The invention can also be applied to other similar scenarios in accordance with these drawings on the premise of sexual labor. Unless otherwise apparent from the language environment or otherwise stated, the same reference numerals in the drawings represent the same structure and operation.

Figure 1 is a schematic diagram of an exemplary system configuration of an information analysis system. The example system configuration 100 can include, but is not limited to, one or more information analysis systems 101, one or more networks 102, and one or more information sources 103. The information analysis system 101 can be used in a system for analyzing and processing the collected information to generate an analysis result. The information analysis system 101 can be a server or a server group. A server group can be centralized, such as a data center. A server group can also be distributed, such as a distributed system. The information analysis system 101 can be local or remote. Network 102 can provide a conduit for information exchange. Network 102 can be a single network or a combination of multiple networks. Network 102 may include, but is not limited to, one or more combinations of a local area network, a wide area network, a public network, a private network, a wireless local area network, a virtual network, a metropolitan area network, a public switched telephone network, and the like. Network 102 may include a variety of network access points, such as wired or wireless access points, base stations, or network switching points, through which the data sources connect to network 102 and transmit information over the network. The information source 103 can provide various information. The information source 103 can include, but is not limited to, a server, a communication terminal. Further, the server may be a web server, a file server, a database server, an FTP server, an application server, a proxy server, etc., or any combination of the above. The communication terminal may be a mobile phone, a personal computer, a wearable device, a tablet computer, a smart TV, or the like, or any combination of the above communication terminals. The information source 103 can send or/and collect information to the information analysis system 101 via the network 102. The information source 103 can be information input by the user or information provided by other databases or information sources.

Figure 2 shows a block diagram of the information analysis system. Information analysis system 101 may include, but is not limited to, one or more collection modules 201, one or more processing modules 202, one or more natural language processing modules 203, one or more flyback modules 204, one or more system databases 205. Some or all of the above modules may be connected to the network 102. The above modules can be centralized or distributed. One or more of the above modules may be local or remote. The collection module 201 can be mainly The means for collecting the required information in various ways, collecting the information may be direct (eg, directly from the one or more information sources 103 via the network 102) or indirectly (eg, through the processing module 201, nature) The language processing module 202, the backtest module 204 or the system database 205 to obtain information). The processing module 202 can be mainly used for pre-processing of information. The pre-processing of information can be manual or automatic. The pre-processing of information can include but is not limited to format conversion, word segmentation, entity identification, number and unit normalization. One or more combinations of processing, text categorization, event attribute extraction, refinement event recognition, and encrypted document decryption. The natural language processing module 203 can be mainly used to generate natural language sentences, and can also receive input natural language sentences. The manner in which the natural language processing module 203 processes the information may be manual or automatic. The flyback module 204 can be primarily used to analyze information. The analysis method can include, but is not limited to, one or more combinations of system definitions, user-defined selections, machine learning, and the like. Information analysis can be done manually, automatically, or a combination of both. System database 205 can be broadly referred to as a device having a storage function. The system database 205 is primarily used to store data collected from the information source 103 and various data generated in the operation of the information analysis system 101. System database 205 can be local or remote. The connection or communication between the system database and other modules of the system can be wired or wireless.

The collection module 201 can transmit the collected information to the processing module 202. The collection module 201 can also transmit the collected information to the natural language processing module 203. The collection module 201 can also transmit the collected information to the backtest module 204. The collection module 201 can receive the request sent by the processing module 202, and can also access the system database 205 according to the request to obtain the required data. After the required data is acquired, the collection module 201 can transmit the data to the processing module 202. The collection module 201 can receive the request sent by the natural language processing module 203, and can also access the system database 205 according to the request to obtain the required data. After the required data is acquired, the collection module 201 can transmit the data to the natural language processing module 203. The collection module 201 can receive the request sent by the backtest module 204, and can also access the system database 205 according to the request to obtain the required data. After the required data is acquired, the collection module 201 can transmit the data to the backtest module 204.

The collection module 201 can be used primarily to collect the required information in a variety of ways. The collection module 201 can obtain the required information by sending a request to the information source 103. After the collection module 201 obtains the required information, the obtained information may be processed in the next step or stored in the system database 205. The collection module 201 can also obtain information stored in the system database 205 by sending a request to the system database 205. Alternatively, system database 205 may also send a request directly to information source 103, which may be stored in system database 205. The information source 103 can be a server, a communication terminal, or the like. Further, the server may be a web server, a file server, a database server, an FTP server, an application server, a proxy server, etc., or any combination of the above. The communication terminal may be a mobile phone, a personal computer, a wearable device, a tablet computer, a smart TV, or the like, or various combinations of the above communication terminals. The above required information may include, but is not limited to, one or more of various news, research reports, announcements, messages, reports, notices, essays, periodicals, and the like. The information required above may be information about various industries including, but not limited to, one or more of sports, entertainment, economics, politics, military, culture, art, science, engineering, and the like. The form of the above-mentioned required information may include, but is not limited to, one or more of text, picture, audio, video, and the like. For example, the video news broadcast on a video website, "World Bank lowered the global economic growth forecast to 2.8% this year", a news report reported by a news website, "HSBC China service industry PMI rose to 53.5 in May", a listed company issued by a stock exchange Announcement "A Co., Ltd. Announcement on Signing Major Contracts for Daily Operations" and a football event preview released by a sports event live broadcast platform "This Saturday Chelsea Club will face the rival Arsenal at the Stamford Bridge Stadium at home."

The processing module 202 can communicate bidirectionally with the collection module 201. The processing module 202 can process the information transmitted by the collection module 201. The information processing can include, but is not limited to, one or more combinations of format conversion, text preprocessing, text classification, attribute extraction, and event recognition. The processing module 202 may also send information to the collection module 201, where the information may include, but is not limited to, processed information and control information, which may include, but is not limited to, control information of the information collection manner, control information of the information collection time, Control information such as information collection sources. The processing module 202 can communicate bi-directionally with the natural language processing module 203. The processing module 202 can transmit the processed information to the self. The language processing module 203 can also receive the information sent by the natural language processing module 203. The processing module 202 can communicate bidirectionally with the flyback module 204. The processing module 202 may transmit the processed information to the backtest module 204, and may also receive the information sent by the backtest module 204. Processing module 202 can communicate bi-directionally with system database 205. The processing module 202 may transmit the processed information to the system database 205 for storage, or may send the request information to the system database 205 and receive the information sent by the system database 205 during the processing.

The natural language processing module 203 can send a request to the collection module 201, and the collection module 201 can access the system database 205 or from one or more information sources 103 upon request to obtain the required information. After the required information is obtained, the collection module 201 transmits the information to the natural language processing module 203. Alternatively, after receiving the request sent from the natural language processing module 203, the collection module 201 may also transmit the information in the collection module 201 to the natural language processing module 203, which may be from the information source 103 or the system database. 205. The natural language processing module 203 can send a request to the processing module 202, and the processing module 202 can access the system database 205 according to the request to obtain the required information. After the required information is obtained, the processing module 202 transmits the information to the natural language processing module 203. Alternatively, the processing module 202 may also transmit the information in the processing module 202 to the natural language processing module 203 after receiving the request sent from the natural language processing module 203. Alternatively, the natural language processing module 203 can directly access the system database 205 and send a request to the system database 205 to obtain the required information, which can be transmitted to the natural language processing module 203. Alternatively, system database 205 can send information to natural language processing module 203 without receiving a request. In an embodiment of the present invention, the natural language processing module 203 can directly receive a natural language statement (not shown) from the information source 103, which can be input by the user using an input device, and the input device includes It is not limited to one or more combinations of a keyboard, a mouse, a camera, a scanner, a handwriting tablet, a voice input device, and the like.

The input information of the natural language processing module 203 may be letters, numbers, characters, words, phrases, sentences, paragraphs, chapters, etc., or one or more of them, or by any number of identifier sets, the set of identifiers may Contains one or more semantics. Optional The input information of the natural language processing module 203 may be a customized information type. In some embodiments of the invention, the input information of the natural language processing module 203 may be characterized as a multi-tuple. For example, the input information of the natural language processing module 203 can be characterized as a quad {k, c, u, d}. The parameter k may be configured to represent a source of information, which may include, but is not limited to, a collection module 201, a processing module 202, a system database 205, an information source 103 (not shown), or any combination of the above sources of information. The parameter c can be configured to represent the communication time. For example, the parameter c can be configured to represent the year, month, date, and the like. By giving the parameter c a specific value, the information of the specific time specified by the parameter c will be input to the natural language processing module 203. The parameter u can be configured to represent the user model to be used. The user model is a data processing model with different functions depending on different user needs. The parameter u by default means that no data model is applied. The parameter d can be configured to indicate that information has been generated. The generated information refers to various entities and attributes that the user has generated during the natural language processing, and various entities and attributes will be used in the subsequent natural language processing.

The natural language processing module 203 can process the collected information to generate natural language statements. The generated natural language statement can be transmitted to the backtest module 204 for backtesting. Specifically, the natural language processing module 203 may send a backtesting request to the backtesting module 204. After the request is approved, the natural language processing module 203 inputs the generated natural language statement to the backtesting module 204 for back testing. Alternatively, the natural language processing module 203 may also not send the backtesting request, but directly input the generated natural language statement to the backtesting module 204 for back testing. In an embodiment of the present invention, after receiving the natural language statement input by the natural language processing module 203, the backtest module 204 further processes the natural language statement to generate a database standard access instruction, thereby accessing or retrieving the stored in the corresponding database. historical data.

In another embodiment of the present invention, the natural language processing module 203 can receive the event generated by the processing module 202, and the natural language processing module 203 can assemble the received natural language statement (event), or can be based on the backtesting. Need, plus additional conditions. For example, for individual stock events, you need to add “stock code or abbreviation”; for industry event backtesting, you need to add “single stock corresponding industry”; for whole market events (such as The central bank cuts interest rates. There is no need to add any statements to the backtest.

It should be noted that the above description of the input information to the natural language processing module 203 is only for the purpose of facilitating understanding of the invention and should not be considered as the only feasible embodiment of the present invention. For those skilled in the art, after understanding the basic principles of the required information, various modifications and changes may be made to the content of the required information without departing from the principle, but these modifications and changes may be made. Still within the scope of the above description. For example, the input information of the natural language processing module 203 can be characterized as a binary group, a triplet, a quintuple, a hexadecimal, an N-tuple, etc., or any combination of the above types of information.

The backtest module 204 can send a backtest condition request to the collection module 201, and the collection module 201 can access the system database 205 according to the request to obtain the required information. After the required information is obtained, the collection module 201 transmits the information to the backtest module 204. Alternatively, after receiving the request sent from the backtest module 204, the collecting module 201 may also transmit the information stored in the collecting module 201 to the back testing module 204. The flyback module 204 can send a request to the processing module 202, and the processing module 202 can access the system database 205 to obtain the required information according to the request. After the required information is obtained, the processing module 202 can transmit the information to the backtest module 204. Alternatively, after receiving the request sent from the backtest module 204, the processing module 202 may also transmit the information stored in the processing module 202 to the backtest module 204. The backtest module 204 can send a request to the natural language processing module 203, and the natural language processing module 203 can access the system database 205 to obtain the required information according to the request. After the required information is obtained, the natural language processing module 203 can transmit the information to the backtest module 204. Alternatively, after receiving the request sent from the backtest module 204, the natural language processing module 203 may also transmit the information stored in the natural language processing module 203 to the backtest module 204. Alternatively, the flyback module 204 can directly access the system database 205 and send a request to the system database 205 to obtain the required information, which can be transmitted to the flyback module 204. Alternatively, system database 205 can send information to flyback module 204 without receiving a request. The input information received by the backtest module 204 may include, but is not limited to, one or more combinations of letters, numbers, characters, words, sentences, paragraphs, chapters, natural language statements, and the like. Sources of input information may include, but are not limited to, collection module 201, processing module 202, natural One or more combinations of language processing module 203, system database 205, information source 103, and the like.

System database 205 or other storage devices within the system generally refer to all media that can have read/write capabilities. The system database 205 or other storage devices in the system may be internal to the system or external devices of the system. The connection manner of the system database 205 or other storage devices in the system may be wired or wireless. System database 205 or other storage devices within the system may include, but are not limited to, one or more combinations of hierarchical databases, networked databases, and relational databases. The system database 205 or other storage devices within the system may digitize the information and store it in a storage device that utilizes electrical, magnetic or optical means. System database 205 or other storage devices within the system can be used to store various information such as programs and data. The system database 205 or other storage devices in the system may be devices that store information by means of electrical energy, such as various memories, random access memory (RAM), read only memory (ROM), and the like. The system database 205 or other storage devices within the system may be devices that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, magnetic bubble memories, USB flash drives, flash memories, and the like. System database 205 or other storage devices within the system may be devices that optically store information, such as CDs or DVDs. The system database 205 or other storage devices within the system may be devices that store information using magneto-optical means, such as magneto-optical disks. The access method of the system database 205 or other storage devices in the system may be one or more combinations of random storage, serial access storage, read-only storage, and the like. The system database 205 or other storage devices within the system may be non-persistent memory or permanent memory. The storage device mentioned above is a few examples, and the storage device that the system can use is not limited thereto. The system database 205 or other storage devices in the system may be local, remote, or on a cloud server.

The operation of the information in the system database 205 or other storage devices in the system may include, but is not limited to, one or more combinations of storage, classification, sorting, filtering, and the like. System database 205 or other storage devices within the system can communicate or exchange information with information source 103. System database 205 or other storage devices within the system may receive information from information source 103 and store it in system database 205 or other storage devices within the system. According to the instructions received, Information in the system database 205 or other storage devices within the system can be extracted and passed to the information source 103. The instruction may be directly from the information source 103, or may be from other modules, such as the collection module 201, the processing module 202, the natural language processing module 203, the backtest module 204, and the like. System database 205 or other storage devices within the system can communicate or exchange information with collection module 201. The system database 205 or other storage devices within the system may receive the information collected by the collection module 201 and store it in the system database 205 or other storage devices within the system. Based on the received instructions, the information in the system database 205 or other storage devices in the system can be extracted and passed to the collection module 201. The instruction may be directly from the collection module 201, or may be from other modules, such as the processing module 202, the natural language processing module 203, the backtest module 204, the information source 103, and the like.

System database 205 or other storage devices within the system can communicate or exchange information with processing module 202. The system database 205 or other storage devices within the system can receive the information collected by the processing module 202 and store it in the system database 205 or other storage devices within the system. Based on the received instructions, information in the system database 205 or other storage devices within the system can be extracted and passed to the processing module 202. The instruction may be directly from the processing module 202, or may be from other modules, such as the collection module 201, the natural language processing module 203, the backtest module 204, the information source 103, and the like.

System database 205 or other storage devices within the system can communicate or exchange information with natural language processing module 203. The system database 205 or other storage devices within the system can receive the information collected by the natural language processing module 203 and store it in the system database 205 or other storage devices within the system. Based on the received instructions, information in the system database 205 or other storage devices within the system can be extracted and passed to the natural language processing module 203. The instruction may be directly from the natural language processing module 203, or may be from other modules, such as the collection module 201, the processing module 202, the backtest module 204, the information source 103, and the like.

The information sent by the system database 205 may be information obtained directly from the information source, or may be processed and analyzed. The information processed by the analysis may be information stored in the system database 205 after being processed by the processing module 202, or may be information stored after being processed by the natural language processing module 203. System database 205 or its The way of transferring information between the storage device and other modules may be wired or wireless, and may be direct or indirect, and may be performed simultaneously or sequentially, and may be periodic or aperiodic. And so on.

Obviously, for those skilled in the art, after understanding the principle of the information analysis system and method, it is possible to arbitrarily combine the modules without deviating from the principle, or to form a subsystem to be connected with other modules. Various modifications and changes in form and detail of the application of the above methods and systems are implemented, but such modifications and changes are still within the scope of the above description. For example, the collection module 201, the processing module 202, the natural language processing module 203, the backtest module 204, and the system database 205 may be different modules embodied in one system, or may be one module to implement the above two or more modules. The functions, such as the processing module 202, can collect information and generate natural language statements. The processing module implements the functions of the collection module 201 and the natural language processing module 203 at the same time, and similar modifications are still within the scope of the claims of the present invention.

Figure 3 shows the flow chart of information analysis. The required information is collected from the information source 103 (see Figure 1) in step 301. The information source 103 can include, but is not limited to, a server, a communication terminal. Further, the server may be a web server, a file server, a database server, an FTP server, an application server, a proxy server, etc., or any combination of the above. The communication terminal may be a mobile phone, a personal computer, a wearable device, a tablet computer, a smart TV, or the like, or any combination of the above communication terminals. Further, in step 301, natural language sentences input by the user through various communication terminals can be received. The above required information may include, but is not limited to, one or more of various news, announcements, comments, research reports, blogs, messages, reports, notices, essays, journals, and the like. The information required above may be information about various industries including, but not limited to, one or more of sports, entertainment, economics, politics, military, culture, art, science, engineering, and the like. The form of the above-mentioned required information may include, but is not limited to, one or more of text, picture, audio, video, and the like. For example, the news can be a video news broadcast on a video website, "The World Bank cuts its global economic growth forecast to 2.8% this year", a news report reported by a news website, "HSBC China service industry PMI rose to 53.5 in May", a stock exchange Announcement of the listed company issued “A Company’s Announcement on Signing Major Contracts for Daily Operations”, The football event preview released by a sports event live broadcast platform "This Saturday Chelsea Club will be at the Stamford Bridge at home against the rivals Arsenal". Step 301 can be completed by the collection module 201.

The information collected in step 301 is processed in step 302. Step 302 can be completed by processing module 202. In some embodiments of the invention, the information collected in step 301 may be textual information. The textual information may be derived directly or indirectly from text, audio, video or any combination of the above sources. Further, when the text information is derived from audio, the system can convert the audio into text by speech recognition or subtitle extraction. When text information is derived from video, the system can convert the video into text by speech recognition or subtitle file extraction. The text information can be Chinese, English, German, Spanish, Arabic, French, Japanese, Korean, Russian, Portuguese, etc., or any combination of the above. Further, the text information may be letters, numbers, characters, words, phrases, sentences, paragraphs, chapters, etc., or one or more of them, or a set of any number of identifiers, the set of identifiers may include One or more semantics. The information processing performed by step 302 may include, but is not limited to, one or more of format conversion, word segmentation processing, entity recognition, number and unit normalization processing, text classification, event attribute extraction, refinement event recognition, and the like. Format conversion converts text information in various formats into a uniform text format. The format of the text information may include, but is not limited to, pdf, doc, epub, mobi, caj, kdh, nh, etc., or one or more of the above formats. The unified text format may include, but is not limited to, one or more combinations of txt, ASCII, MIME, and the like.

Word segmentation can extract words in text information according to word type. Word types can include but are not limited to nouns, verbs, adjectives, adverbs, auxiliary words, onomatopoeia, numbers, proprietary symbols, etc., or one or more of them. . Alternatively, the text information can also be processed using a certain word segmentation algorithm. The word segmentation algorithm may include, but is not limited to, a word segmentation based word segmentation method (ie, mechanical segmentation method), an understanding based word segmentation method, a statistical based word segmentation method, or the like, or one or more of the above word segmentation methods. After the word segmentation process is completed, the text information is physically identified. An entity may include, but is not limited to, one or more of a product, an institution name, a person's name, a place name, a time, a date, a currency, a number, a percentage, and the like. Entity recognition methods may include, but are not limited to, hidden Markov models, maximum entropy models, support Vector machine, rule-based recognition method, and statistical-based recognition method, etc., or one or more of them. Specifically, the system can summarize the elements in the past information and define various event categories. For example: diplomacy, finance, sports, politics, science, education, etc., or one or more of them. The above categories may also include sub-categories of several levels, such as financial classes, which may include sub-categories such as stocks, funds, and futures. The above categories may include the completion of the entity identification, the system will normalize the numbers and units in the text information identified by the entity. For example, the “project total investment of 30,000 yuan” was converted into “project total investment of 30,000 yuan”, and “Messi completed the hat trick in the match against Real Madrid at Barcelona” into “Messi’s match against Real Madrid at Barcelona’s home game. Score 3 goals" and so on.

After completing the number and unit normalization process, the system will classify the text information to obtain a large category of text information (such as financial class). In an embodiment of the present invention, the system can access the system database 205 (see FIG. 2 for details), and pass the storage attributes appearing in the text and the attribute features such as the number of category keywords or the preset weights in the database 205 through certain conditions. The calculation method is calculated and classified by calculation. The category keywords may be extracted by a specific method, and the extraction methods may include, but are not limited to, statistically based chi-square statistics, synonym rules, Boolean association rules, position rules, information gain, mutual information, odds ratio, cross entropy A combination of one or more of the methods such as poor information between classes. In another embodiment of the present invention, the system may employ a machine learning based text classification method including, but not limited to, decision trees, Rocchio, Naïve Bayes, neural networks, support vector machines, linear least squares fitting , nearest neighbor algorithm kNN, genetic algorithm, maximum entropy, etc., or one or more of them. That is, by classifying the training of the text labeled with the category label, the classifier is obtained, thereby performing emotional classification on the new text object. Text information can be classified into one or more categories. A category can be a predefined category of the system and can contain several levels of subclasses. For example, in the financial field, text information can be divided into but not limited to announcements, news, research, blogs, forums, microblogs, and interactive investment. Announcement classes can include, but are not limited to, subclasses such as contract, annual, and emotional. Optionally, the announcement class may include, but is not limited to, periodic reports and equity distribution announcements, transaction announcements, fundraising announcements, major events, policy preferential announcements, executive change announcements, acquisition repurchase announcements, and the like. The system can divide news into news sources. Reliable sources and unreliable sources, such as the official information source CCTV financial channel can be considered as a reliable source of information, news types can include but not limited to financial, political, science and education, political and legal, social, sports, military, entertainment One or more combinations of classes. It is to be noted that the above description of the classification is merely for the purpose of facilitating the understanding of the invention and should not be construed as the only embodiment of the invention. The classification step is not essential in the present invention. For some text information, the system can directly determine its category, and thus the classification step can be skipped. For example, if the title of a certain information is displayed as "A Company's Announcement on Signing a Major Operation Contract for Daily Operations," the system can directly determine that it is an announcement type.

After the text classification is completed, the attributes in the text information can be extracted. Attributes are descriptions of the nature and relationships of entities. For example, Figure 19 shows the cover and excerpts of the announcement of CITIC Securities Co., Ltd. Annual Report 2014. For this announcement, the extracted entity can be “CITIC Securities”, which can be extracted from the table in Figure 19. Attributes include operating income, net profit increase and decrease over the same period, total assets, total liabilities, total shareholders' equity, etc., or one or more of them. After the attribute extraction is completed, the system can combine the entity and the attribute according to a certain rule method to generate a refinement event. For example, for the aforementioned entity “CITIC Securities”, it can combine the “net profit increase and decrease” of one of its attributes to generate a detailed event of “CITIC Securities' 2014 annual net profit growth of 116.2%”. After the refinement event is obtained, the system may generate a natural language statement according to the obtained refinement event (step 303), such as “the net profit growth of CITIC Securities annual report is greater than 100%”, or may be for individual stock events, industry events and full market events. Natural language statements, such as "CITIC Securities annual report net profit growth rate greater than 100%, brokerage industry annual report net profit growth rate greater than 100%, annual report net profit growth rate greater than 100%.". Step 303 can be completed by natural language processing module 203. The generated natural language statement can be input into an analysis system (eg, flyback module 204) to analyze the identified event (step 304). In one embodiment of the invention, step 304 may be performed by the backtest module 204. The above analysis may include, but is not limited to, backtesting the event. Backtesting refers to combining events and related historical events and data according to certain rules to generate a backtest report for users to refer to when investing.

It should be noted that the above description of the information analysis system process is only for the sake of The invention is not to be considered as the only possible embodiment of the invention. The system can also convert the collected information directly into a natural language statement (step 303) and then analyze the natural language statement (step 304). Alternatively, the system may also directly analyze the collected information (step 304).

FIG. 4 is a schematic structural view of the collection module 201. The collection module 201 can include, but is not limited to, one acquisition unit 401, one processing unit 402, and one storage unit 403. The collection unit 401 can collect the required information from the information source 103 (see FIG. 2), or other modules in the system (eg, the processing module 202, the natural language processing module 203, the backtest module 204, the system database 205). The above required information may include, but is not limited to, one or more of various news, announcements, comments, research reports, blogs, messages, reports, notices, essays, journals, and the like. The information required above may be information about various industries including, but not limited to, one or more of sports, entertainment, economics, politics, military, culture, art, science, engineering, and the like. The form of the above-mentioned required information may include, but is not limited to, one or more of text, pictures, audio, video, etc. For example, the news may be a video news broadcast by a video website. The World Bank lowered the global economic growth forecast to 2.8% this year. ", a news report reported by a news website, "HSBC China's service industry PMI rose to 53.5 in May", a listed company announcement issued by a stock exchange, "A company's announcement on signing a major contract for daily operations", a live broadcast of a sports event The platform released the football event preview "This Saturday Chelsea club will be at the Stamford Bridge at home against the rivals Arsenal" and so on. Alternatively, the collection unit 401 may directly receive information input by the user, and the information may include, but is not limited to, a natural language sentence, a program language, and the like.

Processing unit 402 can process the collected information. The processing may include, but is not limited to, storing the collected information in the storage unit 403, storing the collected information in the system database 205, retrieving information from the storage unit 403, and transmitting the information to other modules (eg, the processing module 202, The natural language processing module 203, the backtest module 204, the system database 205) retrieves information from the system database 205 and transmits the information to other modules (eg, the processing module 202, the natural language processing module 203, and the backtest module 204). Optionally, the processing unit 402 can also directly send the collected information to other modules, such as the processing module 202, the natural language processing module 203, the backtest module 204, and the system database. 205. The storage unit 403 can store the information collected by the collection module 201. The storage unit 403 can store information processed by the processing unit 402.

The above description of the collection module is merely a specific example and should not be considered as the only feasible implementation. Obviously, for those skilled in the art, after understanding the basic principles of the required information, various modifications and changes may be made to the content of the required information without departing from the principle, but these corrections And changes are still within the scope of the above description.

FIG. 5 is a schematic diagram showing the structure of the processing module 202. The processing module 202 can include, but is not limited to, a format conversion module 501, a text preprocessing module 502, a text classification module 503, an attribute extraction module 504, and an event recognition module 505. Each of the above modules may be independent, or some modules may be combined into one module. In the processing module 202, the format conversion module 501 can perform format conversion on the information collected by the collection module 201. Format conversion can be done automatically by the system or manually. Format conversion can be done in real time or at regular intervals. File formats for information that can be converted include, but are not limited to, pdf, doc, docx, epub, mobi, caj, kdh, nh, bmp, jpg, tiff, gif, pcx, tga, exif, fpx, svg, psd, cdr, pcd One or more combinations of dxf, ufo, eps, ai, raw, mpeg, avi, mov, asf, wmv, navi, 3gp, RA, RAM, mkv, flv, rmvb, WebM, and the like. For example, the collected information is in the picture jpg format. If the picture contains text information, the picture can be converted into a text format, such as a txt format, by OCR (Optical Character Recognition) recognition.

The text pre-processing module 502 can pre-process the format-converted text, and the pre-processing can include, but is not limited to, one or more of text segmentation, entity recognition, normalization processing, and the like. The text classification module 503 can classify the pre-processed text, and the pre-processed text can be divided into, but not limited to, an announcement type, a news category, a research report class, a blog class, a forum class, a microblog class, and an interactive investment class. . Announcement classes may include, but are not limited to, contract classes, annual report classes, and the like. Alternatively, the announcement category may include, but is not limited to, a reorganization announcement, an equity incentive announcement, a major contract announcement, a policy offer announcement, an executive change announcement, an acquisition repurchase announcement, and the like. The system can divide news into reliable sources and unreliable based on news sources. Source, such as the official information source CCTV financial channel can be considered as a reliable source of information, news types can include but not limited to financial, political, science and education, political and legal, social, sports, military, entertainment, etc. or A variety of combinations. The attribute extraction module 504 can automatically match the attributes related to the extraction event, and the extraction rules can be configured by the system or manually. The event identification module 505 can derive the final refinement event based on the results of the attribute extraction module 504, the results of the text pre-processing module 502, the system database 205, and certain rules.

FIG. 6 is a schematic structural diagram of the format conversion module 501. The format conversion module 501 can include, but is not limited to, a control unit 601, a text processing unit 602, a picture processing unit 603, an audio processing unit 604, and a video processing unit 605. The control unit 601 can select a corresponding processing unit according to the information collected by the collection module 201; the text processing unit 602 can process the information in the text format collected by the collection module 201. The picture processing unit 603 can process the information of the picture format collected by the collection module 201. The audio processing unit 604 can process the information of the audio format collected by the collection module 201. The video processing unit 605 can process the information in the video format collected by the collection module 201. The above units in the embodiments of the present specification may be independently distributed, but in some embodiments, the above partial units may be combined into one unit, for example, the audio processing unit 604 may be combined with the video processing unit 605 to form an audio and video processing. Unit to achieve the functions of both.

The control unit 601 can perform type determination on the information collected by the collection module 201, and select a corresponding processing unit according to the type. For example, after the control unit 601 determines the information collected by the collection module 201 and determines that it is the text format information, the selection text processing unit 602 performs the processing of the next step.

The text processing unit 602 can process the information in the text format collected by the collection module 201 and convert the text data into a unified format. Specifically, the text format in the information collected by the collection module 201 may include, but is not limited to, Hypertext Markup Language (html), Extensible Hypertext Markup Language (xhtml), and expandable. Extensible Markup Language (xml), pdf format (Portable Document) The text processing unit 602 can convert the above format into a unified text format, and the unified text format can include, but is not limited to, the txt format, in one or more of Format, doc, and docx formats (a proprietary format of Microsoft Corporation). For example, the information collected by the collection module 201 is the “2014 Annual Report of CITIC Securities Co., Ltd.”, as shown in FIG. 19, the format of the announcement is in pdf format, and the text processing unit 602 can convert the announcement from pdf format to txt format. format.

The picture processing unit 603 can process the information of the picture format collected by the collection module 201 and convert it into a unified text format. Specifically, the information in the picture format collected by the collection module 201 may be a book, a newspaper, a magazine, a letter, or the like. The image processing unit 603 may use an OCR (Optical Character Recognition) technology to take a picture. The information is converted to a uniform text format.

The audio processing unit 604 can process the data in the audio format in the information collected by the collection module 201 and convert it into a unified text format. Specifically, the audio format in the information collected by the collection module 201 may include, but is not limited to, one or more of CD, WAVE, AIFF, AU, MPEG, MP3, MIDI, WMA, RealAudio, VQF, OggVorbis, AAC, APE, and the like. Alternatively, the audio processing unit 604 can convert it to a text format using speech recognition technology. Speech recognition techniques may include, but are not limited to, methods based on vocal tract model and speech knowledge, methods of pattern matching, and one or more of methods using artificial neural networks, or any combination of the above.

The video processing unit 605 can process the data in the video format in the information collected by the collection module 201 and convert it into a unified text format. Specifically, the video format in the information collected by the collection module 201 may include, but is not limited to, one or more combinations of Flash Video, AVI, WMV, MPEG, Mastroska, Real Video, QuickTime File Format, Ogg, MOD, and the like. The video processing unit 605 can perform text export on the subtitle portion in the video, the subtitle includes the video built-in subtitle and the external subtitle, and is converted into a unified text format. The video processing unit 605 can also extract the audio portion of the video and perform speech recognition to convert it into a uniform text format. In a specific embodiment, if the video is not equipped with a subtitle portion, the video processing unit 605 can extract the audio portion of the video for speech recognition and convert it into a unified text format if the video is loaded with words. Cursor, the subtitles are exported and converted into a unified text format, or you can choose to extract the audio part for speech recognition and convert to a unified text format.

The four processing units included in the format conversion module 501, namely, the text processing unit 602, the picture processing unit 603, the audio processing unit 604, and the video processing unit 605 may not be all included in some embodiments, and may include only one of the units. It can also be some of these units. In some embodiments, the above four processing units are all included, and the order of execution between the processing units may be sequentially performed, may be performed simultaneously, or may be any suitable order. The format conversion module 501 converts the information collected by the collection module 201 into a unified text format, and the text preprocessing module 502 performs subsequent processing on the information in the text format.

The above description is only a specific embodiment of the invention and should not be considered as the only embodiment. It is apparent to those skilled in the art that various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention. Changes are still within the scope of the claims of the present invention.

FIG. 7 is a schematic structural diagram of a text preprocessing module 502. The text pre-processing module 502 can include, but is not limited to, a language recognition unit 701, a text segmentation unit 702, an entity identification unit 703, and a normalization unit 704. The language recognition unit 701 can perform language recognition on the text information processed by the format conversion module 501. The text segmentation unit 702 can perform word segmentation on the text. The entity identification unit 703 can identify an entity in the text. The normalization unit 704 can perform unified normalization processing on the content containing the digital information in the text and its corresponding units to form a standard digital data format. The above units may be independent or may be combined into one unit. For example, the language recognition unit 701 can be combined with the text segmentation unit 702 into one unit.

The language recognition unit 701 can perform language recognition on the text processed by the format conversion module 501. The language used by the collection module 201 may include, but is not limited to, one or more of Chinese, English, French, Russian, Spanish, Arabic, Japanese, German, etc., and the language recognition unit 701 may identify the collection module 201 to collect Information office The language used.

The text segmentation unit 702 can perform word segmentation processing on the text recognized by the language recognition unit 701 by using a certain word segmentation algorithm. The language used by the collection module 201 includes language in words, such as English, French, Russian, etc., where there is a natural separation between words and words; also includes words in words, words It is composed of words, and there is no natural separation between words and words, such as Chinese. Therefore, before the word frequency statistics of Chinese texts are performed, the Chinese text must first be processed by word segmentation, while the English text is not required. The word segmentation algorithm may include, but is not limited to, a word segmentation based word segmentation method (ie, mechanical segmentation method), an understanding based word segmentation method, a statistical based word segmentation method, or the like, or any combination of the above several word segmentation methods. One embodiment of the present invention is a method combining a statistical-based word segmentation method and a dictionary-based word segmentation method. The text segmentation unit 702 can send a request to the system database 205 to access the dictionary database, and when the system database 205 receives the request, the requested dictionary can be sent to the text segmentation unit 702. The dictionary may be a dictionary for a specific domain, and may be, for example, a dictionary for an announcement or a dictionary for news. Specifically, the dictionary may be a dictionary for reorganization announcements, a dictionary for incentive announcements, a dictionary for major contract announcements, a dictionary for policy offer announcements, a dictionary for executive change announcements, a dictionary for purchase repurchase announcements, and the like. The text segmentation unit 702 can obtain the final text segmentation result by combining the statistically obtained word segmentation result with the word segmentation result obtained by the dictionary matching.

The entity identification unit 703 can perform entity identification on the word-processed text by the entity identification method, and can store the identified entity set in the entity database in the system database 205. The entity may include, but is not limited to, one or more of a product, an organization name, a person's name, a place name, a time, a date, a currency, a number, a percentage, etc., for example, CITIC Securities Co., Ltd. 2014 Annual Report The entities that can be identified in the title information are “CITIC Securities”, “Company”, “2014” and “Annual Report”. The entity identification method may include, but is not limited to, a hidden Markov model, a maximum entropy model, a support vector machine, a Boolean association rule, a synonym-based configuration rule, a location rule-based recognition method, and a statistical-based recognition method, or the like. Any combination of identification methods.

The normalization unit 704 can normalize the numbers in the text and their units Rational, so that it has a consistent unit. For example, the normalization unit 704 can convert the "five percent probability of rising" appearing in the text to "the probability of rising 5%", and convert the "total investment of 30,000 yuan" into "the total investment of the project is 30,000 yuan. "Wait.

The four processing unit execution sequences included in the text preprocessing module 502 may be, in order, a language recognition unit 701, a text word segmentation unit 702, an entity recognition unit 703, and a normalization unit 704. The execution order of the processing units in the text preprocessing module 502 may also be first performed by the language recognition unit 701, and it is determined whether the text segmentation unit 702 is executed according to the recognition result of the language recognition unit 701. When the recognition result is Chinese text, the text segmentation unit 702 is executed. When the recognition result is another language having a fixed separator, such as English, Korean, Russian, etc., the text segmentation unit 702 may not execute. The order of execution of the subsequent entity identification unit 703 and the normalization unit 704 may be sequential, may be reversed, or may be performed simultaneously. The text pre-processing module 502 can pre-process the text information processed by the format conversion module 501, and the text classification module 503 can perform subsequent processing on the pre-processed text. The above description is only a specific embodiment of the invention and should not be considered as the only embodiment. It is apparent to those skilled in the art that various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention. Changes are still within the scope of the claims of the present invention.

FIG. 8 is a schematic diagram showing the structure of the text classification module 503. The text classification module 503 can include, but is not limited to, one or more keyword extraction units 801, one or more classification units 802. The keyword extracting unit 801 can perform keyword extraction on the text processed by the text preprocessing module 502. Classification unit 802 can classify the extracted keywords according to predefined rules. The above units may be independent or may be combined into one unit.

The keyword extracting unit 801 can analyze the text processed by the text preprocessing module 502 and extract keywords. The extraction method of the keyword may include, but is not limited to, a method based on statistical chi-square statistics, synonym rules, Boolean association rules, position rules, information gain, mutual information, probability ratio, cross entropy, inter-class information difference, or the like. Any combination. Specifically, for the "2014 Annual Report of CITIC Securities Co., Ltd. In the announcement, the keyword extracting unit 801 first performs keyword extraction, and the extracted keywords may include, but are not limited to, "CITIC Securities", "2014", "annual report", "net profit", "synchronous increase and decrease" "and many more.

The classification unit 802 can classify the text and paste the category label according to a certain classification method by using the keyword proposed by the keyword extraction unit 801. Classification methods may include, but are not limited to, decision trees, Rocchio, Naïve Bayes, neural networks, hidden Markov models, support vector machines, linear least squares fits, nearest neighbor algorithm kNN, genetic algorithms, maximum entropy, etc., or Any combination of the above methods. Classification unit 802 can send a keyword database access request to system database 205. After receiving the request, the system database 205 sends the requested keyword to the classification unit 802. The classifying unit 802 can match the keywords extracted by the keyword extracting unit 801 and the keywords sent by the system database 205 according to a certain algorithm, classify the text according to the matching result, and paste the text with the corresponding category tag. Specifically, in response to the above-mentioned full-text announcement entitled "CITIC Securities Co., Ltd. 2014 Annual Report", the classification unit classifies it as a bulletin category, an annual report subcategory, and labels it. A text can be attributed to different categories according to the matching result. In this case, just paste two labels on the text, and one text can have more than two labels at the same time.

Text classification module 503 is optional in some embodiments. For example, if the information elements in the information collected by the collection module 201 are already determined, the text classification step can be skipped. Specifically, if the information collected is a newsletter, the content of the newsletter is “The 18th Shanghai International Film Festival was closed on the evening of June 21, 2015. The main competition unit Golden Jubilee Award was announced. The Chinese film “The Sunshine "Heart" became the biggest winner, Deng Chao, Guo Tao, Duan Yihong and the three won the same film, Cao Baoping won the best director. The elements of the newsletter, such as time, characters, events, etc., are clear and clear, and the text sorting module 503 can be skipped directly to the next module attribute extraction module 504 for subsequent processing. The execution order of the keyword extraction unit 801 and the classification unit 802 included in the text classification module 503 may be sequential, that is, the keyword extraction unit 801 is executed first, and the classification unit 802 is executed later. The above description is only a specific embodiment of the invention and should not be considered as the only embodiment. Obviously, it will be possible for a person skilled in the art to understand without departing from the scope and principles of the present invention. Various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention.

FIG. 9 is a schematic structural diagram of the attribute extraction module 504. The attribute extraction module 504 can include, but is not limited to, one or more keyword extraction units 901, one or more attribute extraction templates 902, and one or more attribute extraction units 903. The keyword extraction unit 901 can perform keyword extraction on the text; the attribute extraction template 902 can store an extraction rule capable of extracting event attributes; the attribute extraction unit 903 can complete the extraction of the event attributes. The above units may be independent or may be combined into one unit.

The keyword extracting unit 901 may analyze and extract keywords processed by the text preprocessing module 502, and the extraction methods may include, but are not limited to, statistically based chi-square statistics, synonym rules, Boolean association rules, position rules, and information gains. , mutual information, odds ratio, cross entropy, inter-class information difference, etc., or any combination of the above methods. The keyword extraction unit 901 is optional in some embodiments. Since the text classification module 503 is optional, when the text classification module 503 needs to be directly processed by the attribute extraction module 504, the keyword extraction unit 901 can be executed to perform keyword extraction on the preprocessed text. . If the text classification module 503 has been executed, the keyword extraction unit 901 may be skipped.

The attribute extraction template 902 can store extraction rules that are capable of extracting event attributes. Events are made up of attributes of entities and entities. The entity identification unit 703 in the text pre-processing module 502 has performed entity recognition on the text and formed a set of entities, and the attribute extraction template 902 stores different attribute extraction rules for different entities. The extraction rules are pre-configured. The configuration method may be manual configuration, and different entity attribute extraction rules are set for each type of text according to the type of text set in advance. The configuration method can also be a machine learning method. For example, a batch of training text can be selected first. The training text is a clearly typed text that is manually labeled. Through the training of the training text, the attribute extractor (not shown) is obtained. The attribute extractor extracts the required attributes according to different text categories, and then uses the attribute extractor to perform attribute extraction on the new text. The attribute extraction template 902 is optional in some embodiments. When the text classification module 503 is not There is execution, there is no corresponding attribute extraction template without text classification.

The attribute extraction unit 903 can complete the work of extracting attributes from the text. After the text classification module 503 is executed, the attribute extraction unit 903 can select a corresponding attribute extraction template according to the result of the text classification and the label attached to the text. When the text has two or more labels attached, the corresponding number of attribute extraction templates can be selected at the same time, and then the attributes are extracted from the text, and the obtained results are clustered. Specifically, for the information of "CITIC Securities Co., Ltd. 2014 Annual Report", the text classification module 503 assigns it a sub-category of the annual category of the announcement category. According to the label, the attribute extraction unit 903 selects a corresponding attribute extraction template, and extracts an event attribute according to the selected template. Event attributes may include, but are not limited to, information on the amount of operating income and the year-on-year increase, the amount of net profit, the amount of total assets, and the magnitude of the increase. For example, for the above announcement, the attribute information of “net profit attributable to shareholders of the parent company/current period growth (%)/116.20% over the same period of the previous year” can be extracted. The above description is only a specific embodiment of the invention and should not be considered as the only embodiment. It is apparent to those skilled in the art that various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention. Changes are still within the scope of the claims of the present invention.

Returning to FIG. 5, the event recognition module 505 can complete the identification of the event. The entity identification unit 703 in the text preprocessing module 502 has performed entity recognition on the text and formed a set of entities. The attribute extraction module 504 extracts the required set of attributes from the text. The event recognition module 505 can identify the event according to a certain event recognition template according to the entity recognition result and the attribute extraction result, and generate a refinement event. Specifically, for the CITIC Securities Co., Ltd. 2014 Annual Report, the entities identified by the aforementioned text preprocessing module 302 include "CITIC Securities", "Company Co.," "2014," "Annual Report," Net profit", "synchronous increase and decrease", etc., an attribute attribute extracted by attribute extraction module 304 is: "net profit attributable to shareholders of the parent company / current period increase or decrease (%) / 116.20% over the same period of the previous year", event The identification module 505 can obtain the final refinement event according to the entity identification result and the attribute extraction result: "The annual profit growth rate of the net profit attributable to the parent company shareholders of the CITIC Securities 2014 annual report is equal to 116.2%."

In some embodiments, when the collection module 201 collects information input by the user and backtests the information, there may be complex logic, and the event recognition module 505 cannot separately identify the event based on the entity information and the attribute information. . At this time, it is necessary to identify the event according to the event library (event attribute database 1405 or event identification database 1411) in the system database 205 and a certain rule method. For example, when the information input by the user is “a company whose contracted amount accounts for more than 50% of the operating income”, the bidding announcement may not include the data of the company’s operating income. At this time, the final refinement event category can be calculated based on the data of the database (such as historical data) and certain rule methods. The above description is only a specific embodiment of the invention and should not be considered as the only embodiment. It is apparent to those skilled in the art that various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention. Changes are still within the scope of the claims of the present invention

Figure 10 is a flow chart of the processing module. The system performs format conversion on the information transmitted by the collection module 201 and converts it into a unified text format (step 1001). Format conversion may include, but is not limited to, format conversion of one or more of text, pictures, audio, video, and the like. Step 1001 can be implemented by format conversion module 501. The system preprocesses the information in text format (step 1002). Pre-processing may include, but is not limited to, one or more of speech recognition, text segmentation, entity recognition, normalization, and the like. Step 1002 can be implemented by text pre-processing module 502. The system classifies the text (step 1003). The classification step may include, but is not limited to, performing keyword extraction and classification. Step 1003 can be implemented by text classification module 503. The system performs attribute extraction on the text (step 1004). Step 1004 can be implemented by attribute extraction module 504. The system identifies the event (step 1005). Step 1005 can be implemented by event identification module 505. Optionally, the system may directly perform step 1002 without going through step 1001, and directly perform step 1004 without going through step 1003.

FIG. 11 is a schematic structural diagram of the natural language processing module 203. The natural language processing module 203 can include, but is not limited to, one collection unit 1101, and one natural language generation unit 1102. The collecting unit 1101 can access other modules in the system. The block collects the required information (eg, collection module 201, processing module 202, backtest module 204, system database 205). The natural language generating unit 1102 can convert the information collected by the collecting unit 1101 into a natural language sentence. In some embodiments of the invention, the collection unit 1101 may receive the refinement event output by the processing module 202. At the same time, the collecting unit 1101 can also receive user input information from the collecting module 201. The natural language generating unit 1102 can receive the refinement event and process the refinement event according to the user input information. In one embodiment of the present invention, for example, in the field of stocks, for announcements, the user may choose to generate natural language statements for individual stocks, or select natural language statements for the industry, and may also select natural language statements for the entire market. Or one or more of them. For example, in the news field, for IPO new shares, users can choose to generate natural language statements for the market. It should be noted that the above natural language statement can also be automatically generated without the user intervening. For example, for individual stocks, the system can automatically generate natural language statements for individual stocks, natural language statements for the industry, natural language statements for the entire market, or a combination of one or more of them. In the news field, for IPO new shares, the system can automatically generate natural language statements for the market. Further, in the field of news, the natural language generating unit 1102 can generate natural language sentences for commodity prices, natural language sentences for weather conditions, and natural language statements for demographics. The relevant values in the above natural language statements can be defaulted (in the absence of a value, only the change event will be declared). The natural language statement generated by the natural language generating unit 1102 can be input to the backtesting module 204 to backtest the natural language sentence. In still other embodiments of the present invention, the user inputs a natural language statement to be backtested to the collection module 201, and the natural language processing module 203 receives the natural language statement input by the user from the collection module 201. Alternatively, the user can directly input the natural language statement to be returned to the query box of the natural language processing module 203 (not shown). The natural language processing module 203 can preprocess the natural language sentence input by the user to obtain a standard node sequence (the node includes at least an indicator node and a condition node), and construct a node tree according to the relationship between the indicator node and other nodes. The node tree can be used to characterize the combination of indicator conditions. Data query instructions can be generated based on the node tree. The data query command can be input to the backtest module 204 for backtest analysis. The user can call the stored by the natural language processing module 203 Historical data in the system database 205, the user can use Boolean operators (AND, OR, NOT, etc.) to group a certain number of natural language statements together.

It should be noted that the above description of the natural language processing module 203 is merely a specific example and should not be considered as the only feasible implementation. Obviously, for those skilled in the art, after understanding the basic principles of the required information, various modifications and changes may be made to the content of the required information without departing from the principle, but these corrections And changes are still within the scope of the above description. For example, the natural language processing module 203 can also receive the backtest result output by the backtest module 204.

Figure 12 shows the structure of the backtest module. The backtest module 204 can include a standard question unit 1201, a other question unit 1202, an optimization unit 1203, and an extension unit 1204. The standard question unit 1201, the other question unit 1202, the optimization unit 1203, and the extension unit 1204 may be independent. Some of the above units may also be combined into one unit to work.

The standard question unit 1201 can receive natural language statement events of the system standard. In some embodiments of the present invention, the standard question unit 1201 may receive the natural language statement generated by the natural language processing module 203, or may receive natural language statements derived from other modules. Other modules include, but are not limited to, one or more combinations of collection module 201, processing module 202, natural language processing module 203, and system database 205. Other question unit 1202 can receive non-systematic natural language statements. Non-systematic natural language statements may include, but are not limited to, one or more combinations of user input, expert definition, system extracted results, and the like. The standard question unit 1201 and the other question unit 1202 can be combined into one question unit, which can receive natural language sentence events input by the system and the user, and the like.

The optimization unit 1203 can optimize the policy combination according to the received information and the backtesting algorithm. The optimization method can be automatic or manual. For example, in the financial field, the basic backtesting data obtained by the above backtesting algorithm includes but is not limited to the holding period, the single return average, the single return maximum, the single return minimum, the expected annualized return, and the transaction. One or more combinations of the number of times, the profit-loss ratio, the success rate, the maximum revival rate, the weekly win rate, the Sharpe ratio, the maximum number of consecutive consecutive stock-free results, and the average number of stocks per day. The optimization unit 1203 can further provide an optimal strategy according to the backtest result, and also has a report rating. Wait. The extension unit 1204 can be configured to provide a subscription function, and can also be configured to provide an information sharing function. The subscription function may be that the extension unit 1204 subscribes to the information including the specific keyword according to the user's selection, and pushes the information content analyzed by the system to the user in various manners. The sharing function can be that the user shares the information of interest to friends in various ways.

Figure 13 shows the backtest flow chart. Step 1301 receives the information. The received source of information may include, but is not limited to, a collection module 201, a processing module 202, a natural language processing module 203, and a system database 205. The information received can be either a natural language statement or a machine statement. Step 1301 may be that the backtest module 204 receives the natural language statement. Natural language statement information can be directly input by the user or generated by other modules. Step 1301 can be accomplished by standard question element unit 1201 and/or other question element unit 1203.

In step 1302, the received natural language statement and the historical data are back analyzed, and the historical data may be stored in the system database 205 or may be stored in the backtest module 204. Backtesting analysis of natural language statements and historical data can be achieved by certain optimization methods. Optimization methods may include, but are not limited to, one or more combinations of system definitions, user-defined selections, machine learning, and the like.

In step 1303, the information result of the optimized analysis will match the corresponding text template. Text templates can be system-defined or user-defined. The result content matching the template may include, but is not limited to, one or more combinations of backtest reports, report ratings, optimal strategies, trend predictions, and the like.

It should be noted that the above description of the information analysis system flow is only for facilitating understanding of the invention and should not be considered as the only feasible embodiment of the present invention. Obviously, those skilled in the art, after understanding the basic principles required, may make various modifications and changes to the content of the required information without departing from the principle, but these modifications and changes Still within the scope of the above description. For example, in step 1303, the backtested historical information may be matched with a text template, or may be matched with a template such as a voice, a video, or a picture. For another example, the backtesting process may be completed by the backtesting module 204, or may be completed by the natural language processing module 203, the processing module 202, the collecting module 201, and the like.

Returning to FIG. 12, the flyback module 204 can further include an expansion unit 1204. The extension unit 1204 can be configured to provide a subscription function, and can also be configured to provide an information sharing function. The extension unit may include, but is not limited to, various types of application program interfaces (APIs) such as an object-oriented API, a library and framework API, an API and protocol, an API and device interface, a web API, or one or more of them. . The subscription function may be that the extension unit 1204 subscribes to the information including the specific keyword according to the user's selection, and pushes the information content analyzed by the system to the user in various manners. The sharing function can be that the user shares the information of interest to the friend in various ways. The subscription function of the extension unit 1204 may include, but is not limited to, providing push information to the user, and may also recommend users who are interested in similar interests, may also recommend comments of the information, and provide ratings for whether or not the information is helpful. The manner in which the extension unit 1204 pushes may include, but is not limited to, mobile client software, email, SMS, RSS portal, online single-user aggregator, search engine, browser, instant messaging software, social network, and the like. The push period of the extension unit 1204 may be set by the system or may be user-defined. The push cycle can be periodic or irregular. Push can be real-time or delayed. The information content form pushed by the extension unit 1204 may include, but is not limited to, one or more of text, voice, picture, animation, video, and the like. The information content pushed by the extension unit 1204 may include, but is not limited to, information content update that the user has browsed, may be information that the user pays attention to, or may be information recommended by the system according to the user record, or may be a hot situation of the same type of information. One or more.

The sharing function of the extension unit 1204 may be a method of publishing information used by the user, sharing to a designated place, selecting who can view the information, and the like. The content of the information sharing may be a single piece of information or a plurality of pieces of information, and may be information of a part of the selected content or information of the entire content of the page, and may be information content sharing or information comment sharing, and may be information sharing. It can also be informational help rating sharing, etc. The manner of information sharing may include, but is not limited to, one or more of SMS, MMS, email, QQ, MSN, WeChat, Weibo, Douban, Twitter, Facebook, Instagram, everyone, instant messaging software tools, and the like. The information sharing receiving object may include, but is not limited to, one or more of a single friend, a plurality of friends, a circle of friends, a public social circle, a forum, other users, and the like. The content format of information sharing may include, but is not limited to, text, pictures, voice, One or more of animations, videos, web links, and more. The above description of the functions implemented by the information sharing mode is merely a specific example and should not be regarded as the only feasible implementation. The above description of the extension unit 1204 is merely a specific example and should not be considered as the only feasible implementation. Obviously, for those skilled in the art, after understanding the basic principle of the extension unit 1204, the specific manner and steps of implementing the extension unit and the functions that the extension unit can implement without departing from this principle may be Various modifications and changes in form and detail are made, but such modifications and changes are still within the scope of the above description.

Figure 14 is a block diagram of the system database 205. The system database 205 can include, but is not limited to, an original information database, a text database, a text pre-processing database, an entity database, an event attribute database, a keyword database, a text classification database, a historical information database, a natural language. Processing one or more combinations of a database, an event identification database, a backtest module database, a text template database, a dictionary database, and the like. The system database 205 can store data and templates as well as data. For example, historical information collected by the historical information database 1409 can be classified and stored in the database. Similarly, when there is any update in the information processing process, the information is also updated in real time in each database, such as the keyword database 1406, and the synonym update is implemented in the database. Obviously, for those skilled in the art, after understanding the database principle of the information analysis system and method, various forms and details of the application fields of the above methods and systems may be implemented without departing from this principle. Corrections and changes, but these corrections and changes are still within the scope of the above description. For example, various types of databases in the system database 205 may be implemented in the collection module 201, the processing module 202, the natural language processing module 203, and the backtest module 204, respectively. The various types of databases in the system database 205 can also be a library function for realizing two or more types of data in the database. For example, the text pre-processing database 1403 can simultaneously store pre-processed data, entity data, event attributes and keywords, etc. The functions of the entity database 1404, the event attribute database 1405, and the keyword database 1406 are implemented.

Figure 15 shows the flow chart of information analysis. The information analysis system collects the required information in step 1501. Step 1501 can be completed by the collection module 201. Above The required information may include, but is not limited to, one or more of various news, announcements, reviews, research reports, blogs, messages, reports, notices, essays, journals, and the like. The information required above may be information about various industries including, but not limited to, one or more of sports, entertainment, economics, politics, military, culture, art, science, engineering, and the like. The form of the above-mentioned required information may include, but is not limited to, one or more of text, picture, audio, video, and the like. In some embodiments of the invention, the information collected by the system at step 1501 may be textual information. The text information includes, but is not limited to, the following formats: pdf, doc, epub, mobi, caj, etc., or one or more of them.

The system may preprocess the textual information at step 1502. Step 1502 can be completed by processing module 202. Text pre-processing may include, but is not limited to, one or more combinations of format conversion, word segmentation processing, entity recognition, number and unit normalization processing, and the like. For example, the information collected by the system in step 1501 is the “2014 Annual Report of CITIC Securities Co., Ltd.” announcement, which is a PDF file and can be downloaded from the website of the Shanghai Stock Exchange. The system converts the announcement into text in txt format through format conversion to facilitate word segmentation and subsequent text processing. At the same time, the table part inside the PDF format is analyzed and processed, and part of the formatting information and context information are retained. After the format conversion is completed, the system will process the word segmentation according to a certain method. The word segmentation process can be performed in accordance with a statistical model and a dictionary database 1407. Alternatively, word segmentation can also be implemented by applying certain rules. Rules may include, but are not limited to, synonym configuration, Boolean association rules, location rules, or one or more of them. After the word segmentation process is completed, the system will identify the entity. Entity identification includes, but is not limited to, product, institution name, person name, place name, time, date, currency, number, percentage, and the like. Specifically, the system can summarize the elements in the past information and define various event categories. For example: diplomacy, finance, sports, politics, science, education, etc., or one or more of them. The above categories may also include sub-categories of several levels. For example, the financial category includes treasury bonds, stocks, funds, and the like. After the identification is completed, the system will normalize the number and unit of the announcement. For example, convert “3% of net profit growth” to “3% of net profit growth”.

After completing the pre-processing herein, the system will perform text processing on the bulletin (step 1503). Text processing can be done by the processing module 202. After the system is preprocessed Announcement for keyword matching. Keyword matching can be combined with synonym configuration, Boolean association rules, location rules, and the like. Based on the results of keyword matching or other method processing, the system can make a category determination for the announcement (step 1504). For example, in the “2014 Annual Report of CITIC Securities Co., Ltd.” shown in Figure 19, the keywords extracted may be “CITIC Securities”, “2014”, “Annual Report”, “Net Profit”, “Sequential Increase and Decrease”, etc. The announcement can be judged as a broad category of financial reports. Step 1504 can be completed by processing module 202.

After completing the category determination, the system can generate a refinement event in accordance with a rule (step 1505). In some embodiments of the invention, the attributes in the textual information may be extracted after the category determination is completed. An attribute is a description of the nature or relationship of an entity. For example, for the “2014 Annual Report of CITIC Securities Co., Ltd.” shown in Figure 19, the extracted entity can be “CITIC Securities”, and the attributes that can be extracted from the table in Figure 19 can be operating income, net profit. The increase or decrease during the same period, total assets, total liabilities, total shareholders' equity, etc., or one or more of them. After the attribute extraction is completed, the system can combine the entity and the attribute according to a certain rule method to generate a refinement event. Step 1505 can be completed by processing module 202. The rules for generating refinement events may include, but are not limited to, synonym configuration, Boolean association rules, location rules, or one or more of them. For the entity “CITIC Securities” extracted from the above announcement, it can combine the “net profit increase and decrease” of one of its attributes to generate a detailed event of “CITIC Securities' 2014 annual net profit growth of 116.2%”.

After generating the refinement event, the system can generate a natural language statement for the refinement event (step 1506). Step 1506 can be performed by natural language processing module 203. For the above announcement, the system can generate 3 natural language sentences, “the net profit of CITIC Securities annual report increased by more than 100% year-on-year”, “the annual growth rate of net profit of brokerage industry annual report is greater than 100%”, “annual report net profit growth rate is greater than 100% ", that is, corresponding to three levels of individual stocks, industry and the entire stock market. The system can perform individual stock event backtesting, industry event backtesting, and full market backtesting for the three natural language sentences generated above (step 1507). Step 1507 can be completed by the backtest module 204. After the backtest is completed, the system can match the backtest result to the text template to generate a backtest report (step 1508). Step 1508 can be performed by The flyback module 204 is completed. For the above announcement, an example back test report generated by the system may be “nearly one year, all financial report announcements of A shares, the average return of the next day's closing is 0.48%, and the rising probability is 47.77%. Among them, the securities industry has announced 165 similar announcements. The average return on the next day was 0.72%, the probability of increase was 46.67%, the probability of the stock price falling next day was too large, and the probability of profit was low. The optimal strategy: the highest probability of rise is to close the market after 11 days of holding, the average return is 9.40%. The probability of rise is distributed at around 50%, so the judgment is “insignificant”.

It should be noted that the above description of the flow chart of the information analysis system is only for the purpose of understanding the invention and should not be regarded as the only embodiment. It is apparent to those skilled in the art that various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention. And modifications are still within the scope of the claims of the present invention. For example, at step 1504, the system can directly collect information entered by the user and convert the information into a natural language statement (step 1506). Similarly, after the system completes the collection of information, the system can directly perform text processing (step 1503). Similarly, after the text processing is completed, the system can generate a refinement event directly according to the processed text, and step 1504 is not necessary.

Figure 16 shows the flow chart of the interaction of the information analysis system. The information analysis system receives the natural language statement at step 1601. The natural language statement can be directly input by the user, or can be a natural language statement obtained by processing texts such as announcements and news. Specifically, the user can input the natural language statement through the interactive interface provided by the information analysis system (see FIG. 18). Users can enter any natural language statement. For example, in the financial field, the user can enter "000826, sign a major contract"; the system can retrieve the major contract signed by the company corresponding to code 000826 based on the input natural language statement. The numbers and dates of natural language statements can be in any format (see Figure 18).

The information analysis system processes the natural language statement received in step 1601 in step 1602. Processing may include, but is not limited to, word segmentation processing, entity recognition, number and unit normalization processing, text classification, event attribute extraction, refinement event recognition, and the like. The information analysis system backtests the processed natural language statements in step 1602 at step 1603, and then generates a backtest report at step 1604. The details of the backtest report will be described in detail in FIG.

17 is a schematic diagram of an interactive interface of an information analysis system for news or announcements. Referring to FIG. 2, the interactive interface can be generated by the backtest module 204 and used to display backtest results (backtested impressions). The interactive interface can be displayed on a variety of electronic devices. The electronic device may include, but is not limited to, a mobile phone, a personal computer, a tablet, a PDA, a smart watch, a smart home appliance, a smart vehicle, etc., or one or more of them. In the address bar, the user can enter a Uniform Resource Locator (URL) for any announcement or news to read the announcement or news and its analysis results. Alternatively, the user can also enter an IP (Internet Protocol) address for any announcement or news.

In query block 1701, the user can enter the full name of the announcement or news. Alternatively, the user can also enter keywords in the announcement or news to select the specified news or announcement in the results list. After the specified announcement or news is selected, the title, body content, and backtest report of the announcement or news may be displayed on the interactive interface. The area 1702 can display all or part of the body content of the announcement or news, and the user can view the body content through a mouse, a button, a touch screen, a voice control, or a touch pad. Area 1703 can display a backtest report for the selected announcement or news. The backtest report may include, but is not limited to, display of historical data, for example,

"In the past year, all A-shares have signed major contract announcements. The average return on the next day was 0.38%, and the probability of increase was 47.62%.

Among them, XX Pharmaceutical announced 16 similar announcements, with an average return of 0.29% on the next day, with a rising probability of 68.75%. The stock price has a high probability of rising the next day and has a high probability of profit. ”

In addition to presenting historical data, rating and suggestion strategies for announcements or news may also be displayed in area 1703. The proposed strategy is based on the optimal historical performance after the latest time in the backtesting cycle. The suggested strategy may be, for example, the highest probability of rise is to close the market after 1 day of holding, with an average return of 0.29%. Report ratings can be positive, bad or insignificant. Area 1704 can display the latest announcements or news for the user to view. The display mode can be a list display, that is, only the title and time of the latest announcement or news are displayed. The area 1705 can display some relevant information of the selected report or news, such as the selected report or news, the type of announcement (news), the time of publication, the name of the securities involved in the news or announcement, the stock code, the announcement (news) number, and the like.

The above description is only a specific embodiment of the classification display module of the present invention and should not be considered as the only embodiment. It is apparent to those skilled in the art that various modifications and changes in form and detail may be made without departing from the spirit and scope of the invention. Changes are still within the scope of the claims of the present invention.

18 is a schematic diagram of an interactive interface of an information analysis system for user input. Referring to FIG. 2, the interactive interface can be generated by the backtest module 204 and used to display backtest results (backtested impressions). The interactive interface can be displayed on various electronic devices, including but not limited to, a mobile phone, a personal computer, a tablet computer, a PDA, a smart watch, a smart home appliance, a smart vehicle, etc., or one or more of them. In the address bar, the user can enter a uniform resource addresser (URL) of any text to read the text and its analysis results. Alternatively, the user can also input an IP (Internet Protocol) address of any text.

In area 1801, the user can enter any natural language statement. For example, in the financial field, users can enter “December 10, 20-day moving average bonding; amplitude is less than 3%; two ratios are ranked from large to small” or “dividend rate is greater than 3% for two consecutive years; earnings per share is greater than 2 yuan; The market value of less than 5 billion operating income grew from small to large. The numbers and dates in the natural statements you enter can be numbers and dates in any format. The date can be the previous day, last week, last weekend, last week 5, last month, last quarter, big year, near N days, nearly N weeks, nearly N trading days, and so on. The number can be 1, 3, 3, 5, 5, 5%, etc. The number can also be a range, such as 5 to 10 yuan, 5-10%, and the like. A collation can also be added to the input natural language statement. The sorting rule can be, for example, the ratio is from large to small, the quantity ratio is from small to large, the increase and decrease is from small to large, the increase and decrease is from large to small, the turnover rate is from large to small, the turnover rate is from small to large, and the capital is small. Flows from large to small, circulation from small to large, DDE from large to small, market value from small to large, basic earnings per share from large to small, sales gross margin from large to small, sales gross margin from large to small, net assets (Year-year growth rate) from large to small, the return on assets roe from large to small, the interest multiplier has been increased from large to small, operating income (year-on-year growth rate) from large to small, or one or more of them.

In area 1802, the user can set an analysis strategy. For example, the user can set Time range, position stock, buying opportunity, shareholding period, take profit condition, stop loss condition, transaction rate, etc. Specifically, the user can set the buying opportunity to buy after the opening of the next day, or set the take profit condition to “retract 5% take profit when greater than 25%”. After setting the contents in the area 1801 and the area 1802, the user can click on the query button, and a backtest report on the input natural language sentence will be generated. Region 1803 can present a rating for generating a backtest report and a suggested strategy. The report rating can be an estimate of the maximum expected annualized rate of return and maximum success rate. Region 1804 can display a backtest report for the input natural language statement. The backtest report may include, but is not limited to, backtest data analysis, cumulative income graph, revenue distribution graph, historical transaction query, and the like. Backtest data can include, but is not limited to, holding period, single return average, single return minimum, expected annualized return, number of transactions, profit-loss ratio, success rate, maximum volatility, weekly win rate, Sharpe ratio , the maximum number of consecutive consecutive stock selection results, the average number of stocks per day, etc.

The above description of the applicable fields is merely a specific example and should not be considered as the only feasible implementation. Obviously, for those skilled in the art, after understanding the basic principles of an information analysis method and system, it is possible to implement the application form and details of the above method and system without departing from this principle. Various modifications and changes, but such modifications and changes are still within the scope of the above description. Any configuration system that can be organized into data can use the system described in the present invention to implement information analysis functions. For example, the present invention can be used as a browser plug-in. When a user browses a website, information analysis of the current web page news or announcement is required. Use the plug-in to backtest the historical information of the news or announcement, and give predictions; similarly, the system can also be embedded in the company system for intelligent data analysis of financial statements; in addition, various sensors collect data, such as temperature sensors, humidity sensors Wind sensors can read environmental data, and the system can be used to analyze environmental historical trends and predict future environmental changes. In medical terms, the same drug is used in different age groups. The effect is backtested, such as the symptoms of a cold, and it is based on historical data to analyze how many days have passed.

Claims

An information analysis system comprising:

A computer readable storage medium storing the executable module, comprising:

a collection module capable of collecting information;

a processing module, wherein the processing module is capable of pre-processing the collected information, and extracting an event from the pre-processed information;

a natural language processing module capable of generating a natural language statement according to the extracted event;

a backtesting module, wherein the backtesting module is capable of acquiring historical information according to the generated natural language statement, and generating a backtesting result according to the historical information;

A processor capable of executing the executable module of the computer readable storage medium storage.
The information analysis system according to claim 1, further comprising a database capable of storing said collected information, preprocessed information, extracted events, natural language sentences, historical information, and backtest results.
According to claim 2, the database comprises an original information database, a text database, a text pre-processing database, an entity database, an event attribute database, a keyword database, a text classification database, a history information database, a natural language processing database, an event recognition database, Backtest module database, text template database, dictionary database.
The processing module of claim 1, further comprising a format conversion module, a text processing module, an attribute extraction module, and an event recognition module.
The processing module of claim 4 further comprising a text classification module.
According to claim 1, the processing module adopts a method including chi-square statistics, information gain, mutual information, probability ratio, cross entropy, inter-class information difference, keyword statistics, decision tree, Rocchio, naive Bayes, and nerve. Network, support vector machine, linear least squares fit, nearest neighbor algorithm kNN, genetic algorithm, sentiment classification, maximum entropy, Generalized Instance Set, synonym configuration, Boolean association rules, position rules, machine learning.
According to claim 1, the natural language processing module can be connected from the collection module Receive information.
According to claim 1, the backtesting module further includes a backtesting information judgment, and the backtesting information determines that the evaluation is given according to the situation of the backtesting result.
According to claim 1, the backtest result can be displayed to the user.
An information analysis method, including:

collect information;

Extracting an event based on the information;

Generating a natural language statement based on the event;

Obtaining historical information according to the natural language statement;

The natural language sentence is back-tested and analyzed in combination with the historical information.
According to claim 10, the collecting information includes collecting user input information and non-user input information, and the non-user input information source includes a communication terminal and a server.
According to claim 11, the collecting information includes collecting announcement information and news information.
According to claim 10, the extraction event further comprises entity identification and attribute extraction.
According to claim 13, the entity identification further includes a format conversion, a text segmentation, a number, and a unit normalization process.
According to claim 13, the attribute extraction can be implemented by a system-defined model.
According to claim 10, the natural language statement can be generated based on user input information.
According to claim 10, the natural language statement can be further extended according to the event category.
According to claim 10, the natural language sentence backtesting can generate a backtest result according to the information category.