slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet
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Updated
Feb 4, 2021 - Python
slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet
This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.
The ATIS (Airline Travel Information System) Dataset
自然语言理解 基准测试 数据集 | Benchmark datasets for Natural Language Understanding (NLU)
Weather-Flight-Bot, a combined model of semantics slot-filling and intent detection powered by neural network (LSTM and CNN layers).
Intent detection and Slot filling joint learning on ATIS and SNIPS datasets using pre-trained models and built-from-scratch models
CKY parser for ATIS grammar
Intent Classification Example Using ATIS dataset
Named Entity Recognition on ATIS dataset with Transformers
The Icelandic translation of the ATIS dataset
Natural Language Understanding(NLU) benchmark for closed domain chatbot and task-oriented artificial intelligence secretary system
In this project, you will learn how to generate a complete semantic parse of utterances. First, you will make a discovery on your dataset to get insights about the dataset analytics. Then, you will learn, to extract entities with two different techniques – with spaCy Matcher and by walking on the dependency tree. Next, you will learn different w…
Projet de Traitement Automatique des Langues sur les données ATIS
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