A very simple framework for state-of-the-art Natural Language Processing (NLP)
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Updated
May 26, 2024 - Python
A very simple framework for state-of-the-art Natural Language Processing (NLP)
中文命名实体识别(包括多种模型:HMM,CRF,BiLSTM,BiLSTM+CRF的具体实现)
NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition
The BiLSTM-CRF model implementation in Tensorflow, for sequence labeling tasks.
Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
Empower Sequence Labeling with Task-Aware Language Model
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling
word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, information extraction (i.e., entity, relation and event extraction), knowledge graph, text generation, network embedding
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
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
Tensorflow implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)
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.
RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, se…
Learning Named Entity Tagger from Domain-Specific Dictionary
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