AREkit (Attitude and Relation Extraction Toolkit) -- is a python toolkit, devoted to document level Attitude and Relation Extraction between text objects from mass-media news and analytical articles with entity-linking (EL) API support for objects.
Is an open-source and extensible toolkit focused on data preparation for document-level relation extraction organization. In complements the OpenNRE functionality since document-level RE setting is not widely explored (2.4 [paper]). The core functionality includes (1) API for document presentation with EL (Entity Linking, i.e. Object Synonymy) support for sentence level relations preparation (dubbed as contexts) (2) API for contexts extraction (3) relations transferring from sentence-level onto document-level, and more. It providers contrib modules of neural networks (like OpenNRE) applicable for sentiment attitude extraction task.
pip install git+https://github.com/nicolay-r/[email protected]
from arekit.data import download_data
download_data()- ARElight [site] [github]
- Infer attitudes from large Mass-media documents or sample texts for your Machine Learning models applications
- Frame-Based attitude extraction workflow for news processing [code]
- Represents an attitude annotation workflow based on RuSentiFrames lexicon which is utilized for news processing;
- Neural Networks Applications in Sentiment Attitude Extraction [code]
- Neural Networks application for attitude extraction from analytical articles;
- BERT-based model utils for Sentiment Attitude Extraction task [code]
- Analytical news formatter for BERT-based models;