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中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
A very simple framework for state-of-the-art Natural Language Processing (NLP)
modest natural-language processing
Stanford CoreNLP: A Java suite of core NLP tools.
Official Stanford NLP Python Library for Many Human Languages
An open source library for deep learning end-to-end dialog systems and chatbots.
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services
Snips Python library to extract meaning from text
农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策
Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
百度NLP：分词，词性标注，命名实体识别，词重要性
中文分词 词性标注 命名实体识别 依存句法分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁 自然语言处理
State of the Art Natural Language Processing
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.
A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)
Named Entity Recognition (LSTM + CRF) - Tensorflow
Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取
Multi-Task Deep Neural Networks for Natural Language Understanding
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.
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
文本挖掘和预处理工具（文本清洗、新词发现、情感分析、实体识别链接、关键词抽取、知识抽取、句法分析等），无监督或弱监督方法
中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）
中文命名实体识别，实体抽取，tensorflow，pytorch，BiLSTM+CRF
A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.
Pytorch-Named-Entity-Recognition-with-BERT
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Add a way to change the sample id output in the annotation process to a specific number (see picture).
Reason: I want to annotate large text and the app don't like it when the documents to annotate are too large, so I spitted in a sentence the document but I would like to be able to