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Natural language processing
来自开放百科 - 灰狐
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==项目== | ==项目== | ||
| − | + | [[文件:gate-logo.png|right|Gate]] | |
*[https://github.com/edobashira/speech-language-processing Speech and Natural Language Processing] [[image:awesome.png]] | *[https://github.com/edobashira/speech-language-processing Speech and Natural Language Processing] [[image:awesome.png]] | ||
| + | *[https://nlpprogress.com/ NLP-progress] Tracking Progress in Natural Language Processing | ||
| + | *[https://github.com/huggingface/transformers Transformers] 为 Jax、[[PyTorch]] 和 [[TensorFlow]] 打造的先进的自然语言处理，提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨让最先进的 NLP 技术人人易用。 | ||
| + | *[https://aclweb.org/aclwiki/Tools_and_Software_for_English Software for Computational Linguistics and Natural Language Processing] | ||
| + | *[https://github.com/RaRe-Technologies/gensim gensim] Topic Modelling for Humans | ||
*[https://github.com/facebookresearch/fastText fastText] | *[https://github.com/facebookresearch/fastText fastText] | ||
*[https://github.com/facebookresearch/DrQA DrQA] Reading Wikipedia to Answer Open-Domain Questions | *[https://github.com/facebookresearch/DrQA DrQA] Reading Wikipedia to Answer Open-Domain Questions | ||
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*[https://github.com/stanfordnlp Stanford NLP @ GitHub] | *[https://github.com/stanfordnlp Stanford NLP @ GitHub] | ||
*[https://github.com/NLPchina NLPChina: 中国自然语言处理开源组织] | *[https://github.com/NLPchina NLPChina: 中国自然语言处理开源组织] | ||
| + | *[https://gate.ac.uk/ GATE] a full-lifecycle open source solution for text processing | ||
| + | *[https://github.com/thunlp THUNLP] Natural Language Processing Lab at Tsinghua University | ||
| + | *[https://github.com/HIT-SCIR/ltp 哈工大 LTP（Language Technology Platform）] | ||
| + | *[https://github.com/languagetool-org LanguageTool] 是一款开源（LGPL）多语言语法、风格和拼写检查器 | ||
==NLU & Bot== | ==NLU & Bot== | ||
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*[https://github.com/botpress/botpress Botpress] The open-source bot platform, [[TypeScript]]和[[JavaScript]]驱动 | *[https://github.com/botpress/botpress Botpress] The open-source bot platform, [[TypeScript]]和[[JavaScript]]驱动 | ||
*[https://github.com/Urinx/WeixinBot WeixinBot] 网页版微信API，包含终端版微信及微信机器人 | *[https://github.com/Urinx/WeixinBot WeixinBot] 网页版微信API，包含终端版微信及微信机器人 | ||
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| + | ==机器翻译== | ||
| + | [[Machine translation]] | ||
==开放数据== | ==开放数据== | ||
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==文档== | ==文档== | ||
| + | *[https://aclweb.org/aclwiki/Best_paper_awards ACL,NAACL,EMNLP,IJCNLP Best paper awards] | ||
*[http://docs.huihoo.com/deep-learning/Deep-Learning-for-Natural-Language-Processing-CCF-ADL-20160529.pdf Deep Learning for Natural Language Processing] | *[http://docs.huihoo.com/deep-learning/Deep-Learning-for-Natural-Language-Processing-CCF-ADL-20160529.pdf Deep Learning for Natural Language Processing] | ||
*[http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/NLP-and-Deep-Learning-1-Human-Language-and-Word-Vectors.pdf NLP and Deep Learning 1: Human Language & Word Vectors] | *[http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/NLP-and-Deep-Learning-1-Human-Language-and-Word-Vectors.pdf NLP and Deep Learning 1: Human Language & Word Vectors] | ||
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image:nlp-nlu-engineer-skill-tree.png|技能树 | image:nlp-nlu-engineer-skill-tree.png|技能树 | ||
image:AnyQ-Framework.png|AnyQ问答系统框架 | image:AnyQ-Framework.png|AnyQ问答系统框架 | ||
| + | image:gate-apis.png|Gate | ||
| + | image:ltp-framework.png|LTP | ||
</gallery> | </gallery> | ||
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*[https://www.jiqizhixin.com/articles/2018-12-19-17 一文详解维基百科的开放性问答系统] | *[https://www.jiqizhixin.com/articles/2018-12-19-17 一文详解维基百科的开放性问答系统] | ||
| + | [[category:computational linguistics]] | ||
[[category:natural language processing]] | [[category:natural language processing]] | ||
[[category:speech recognition]] | [[category:speech recognition]] | ||
| − | [[category: | + | [[category:artificial intelligence]] |
| + | [[category:computer science]] | ||
2022年8月9日 (二) 10:11的最后版本
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您可以在Wikipedia上了解到此条目的英文信息 Natural language processing Thanks, Wikipedia. |
Natural language processing(简称NLP) 自然语言处理是人工智能和语言学领域的分支学科。
目录 |
[编辑] 简介
Deep Learning is becoming hot in Natural Language Processing
[编辑] 项目
- Speech and Natural Language Processing
- NLP-progress Tracking Progress in Natural Language Processing
- Transformers 为 Jax、PyTorch 和 TensorFlow 打造的先进的自然语言处理，提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨让最先进的 NLP 技术人人易用。
- Software for Computational Linguistics and Natural Language Processing
- gensim Topic Modelling for Humans
- fastText
- DrQA Reading Wikipedia to Answer Open-Domain Questions
- Apache UIMA
- DeepDive
- DeepPavlov
- Apache OpenNLP
- ScalaNLP
- Natural Language Toolkit
- Haskell Computational Linguistics（计算语言学）
- Stanford NLP Group, 斯坦福大学自然语言处理工具
- Stanford NLP @ GitHub
- NLPChina: 中国自然语言处理开源组织
- GATE a full-lifecycle open source solution for text processing
- THUNLP Natural Language Processing Lab at Tsinghua University
- 哈工大 LTP（Language Technology Platform）
- LanguageTool 是一款开源（LGPL）多语言语法、风格和拼写检查器
[编辑] NLU & Bot
- BotSharp Conversation as a platform (CaaP) is the future.
- Microsoft Bot Builder
- ChatterBot Python驱动
- Botpress The open-source bot platform, TypeScript和JavaScript驱动
- WeixinBot 网页版微信API，包含终端版微信及微信机器人
[编辑] 机器翻译
[编辑] 开放数据
[编辑] 文档
- ACL,NAACL,EMNLP,IJCNLP Best paper awards
- Deep Learning for Natural Language Processing
- NLP and Deep Learning 1: Human Language & Word Vectors
- NLP and Deep Learning 2: Compositonal Deep Learning
- Deep NLP Recurrent Neural Networks
- Memory, Reading, and Comprehension
- Deep NLP Applications and Dynamic Memory Networks
[编辑] 图书
[编辑] 图集
NLU,NLP,ASR
DrQA
传统NLP和深度学习NLP
NLP应用
技能树
AnyQ问答系统框架
Gate
LTP
[编辑] 链接
- Association for Computational Linguistics
- Overview of Artificial Intelligence and Role of Natural Language Processing in Big Data
- 预测电影评级：NLP正是电影公司所需要的
- 斯坦福大学深度学习与自然语言处理第一讲：引言
- 斯坦福大学深度学习与自然语言处理第二讲：词向量
- 斯坦福大学深度学习与自然语言处理第三讲：高级的词向量表示
- 用MeCab打造一套实用的中文分词系统(一)
- 用MeCab打造一套实用的中文分词系统(二)
- 用MeCab打造一套实用的中文分词系统(三)：MeCab-Chinese
- 用MeCab打造一套实用的中文分词系统(四)：MeCab增量更新
- 无限大地NLP_空木的专栏,研究自然语言处理、机器学习、信息抽取等方向
- 清华大学自然语言处理与社会人文计算实验室
- 一文详解维基百科的开放性问答系统
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