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Natural language processing
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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。 | 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。 | ||
| + | |||
| + | ==简介== | ||
| + | [[deep learning|Deep Learning]] is becoming hot in Natural Language Processing | ||
==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]] | ||
| − | *[http://nlp.stanford.edu/projects/chinese-nlp.shtml 斯坦福大学自然语言处理工具] | + | *[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/DrQA DrQA] Reading Wikipedia to Answer Open-Domain Questions | ||
| + | *[[Apache UIMA]] | ||
| + | *[[DeepDive]] | ||
| + | *[https://github.com/deepmipt/DeepPavlov DeepPavlov] | ||
| + | *[[Apache OpenNLP]] | ||
| + | *[[ScalaNLP]] | ||
| + | *[[Natural Language Toolkit]] | ||
| + | *[[Haskell]] [https://wiki.haskell.org/Applications_and_libraries/Linguistics Computational Linguistics（计算语言学）] | ||
| + | *[http://nlp.stanford.edu/ Stanford NLP Group], [http://nlp.stanford.edu/projects/chinese-nlp.shtml 斯坦福大学自然语言处理工具] | ||
| + | *[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://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== | ||
| + | *[https://github.com/SciSharp/BotSharp BotSharp] Conversation as a platform (CaaP) is the future. | ||
| + | *[https://github.com/Microsoft/BotBuilder Microsoft Bot Builder] | ||
| + | *[https://github.com/gunthercox/ChatterBot ChatterBot] [[Python]]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 | ||
| + | |||
| + | ==机器翻译== | ||
| + | [[Machine translation]] | ||
| + | |||
| + | ==开放数据== | ||
| + | *[https://ai.tencent.com/ailab/nlp/embedding.html 腾讯AI Lab开源大规模高质量800万中文词向量数据] | ||
| + | |||
| + | ==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/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/NLP-and-Deep-Learning-2-Compositonal-Deep-Learning.pdf NLP and Deep Learning 2: Compositonal Deep Learning] | ||
| + | *[http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/Deep-NLP-Recurrent-Neural-Networks.pdf Deep NLP Recurrent Neural Networks] | ||
| + | *[http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/Memory-Reading-and-Comprehension.pdf Memory, Reading, and Comprehension] | ||
| + | *[http://docs.huihoo.com/deep-learning/deeplearningsummerschool/2015/Deep-NLP-Applications-and-Dynamic-Memory-Networks.pdf Deep NLP Applications and Dynamic Memory Networks] | ||
==图书== | ==图书== | ||
*[http://www.nltk.org/book/ 《Natural Language Processing with Python》] | *[http://www.nltk.org/book/ 《Natural Language Processing with Python》] | ||
| + | |||
| + | ==图集== | ||
| + | <gallery> | ||
| + | image:NLU-NLP-ASR.png|NLU,NLP,ASR | ||
| + | image:drqa.png|DrQA | ||
| + | image:difference-between-classical-nlp-deep-learning-nlp.png|传统NLP和深度学习NLP | ||
| + | image:future-applications-of-nlp.png|NLP应用 | ||
| + | image:nlp-nlu-engineer-skill-tree.png|技能树 | ||
| + | image:AnyQ-Framework.png|AnyQ问答系统框架 | ||
| + | image:gate-apis.png|Gate | ||
| + | image:ltp-framework.png|LTP | ||
| + | </gallery> | ||
==链接== | ==链接== | ||
| + | *[https://www.aclweb.org Association for Computational Linguistics] | ||
| + | *[https://www.xenonstack.com/blog/overview-of-artificial-intelligence-and-role-of-natural-language-processing-in-big-data Overview of Artificial Intelligence and Role of Natural Language Processing in Big Data] | ||
| + | *[http://www.infoq.com/cn/articles/predicting-movie-ratings-nlp 预测电影评级：NLP正是电影公司所需要的] | ||
*[http://www.52nlp.cn/?p=8339 斯坦福大学深度学习与自然语言处理第一讲：引言] | *[http://www.52nlp.cn/?p=8339 斯坦福大学深度学习与自然语言处理第一讲：引言] | ||
*[http://www.52nlp.cn/?p=8438 斯坦福大学深度学习与自然语言处理第二讲：词向量] | *[http://www.52nlp.cn/?p=8438 斯坦福大学深度学习与自然语言处理第二讲：词向量] | ||
| 第19行： | 第79行： | ||
*[http://www.52nlp.cn/?p=8660 用MeCab打造一套实用的中文分词系统(四)：MeCab增量更新] | *[http://www.52nlp.cn/?p=8660 用MeCab打造一套实用的中文分词系统(四)：MeCab增量更新] | ||
*[http://blog.csdn.net/u010454729 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] | *[http://blog.csdn.net/u010454729 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] | ||
| + | *[http://www.thunlp.org/ 清华大学自然语言处理与社会人文计算实验室] | ||
| + | *[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: | + | [[category:speech recognition]] |
| + | [[category:artificial intelligence]] | ||
| + | [[category:computer science]] | ||
2022年8月9日 (二) 10:11的最后版本
|
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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