欢迎大家赞助一杯啤酒🍺 我们准备了下酒菜:Formal mathematics/Isabelle/ML, Formal verification/Coq/ACL2, C++/F#/Lisp
Natural language processing
来自开放百科 - 灰狐
(版本间的差异)
小 (→项目) |
小 (→链接) |
||
(未显示1个用户的18个中间版本) | |||
第7行: | 第7行: | ||
==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 | ||
第16行: | 第21行: | ||
*[[ScalaNLP]] | *[[ScalaNLP]] | ||
*[[Natural Language Toolkit]] | *[[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] | *[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/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== | ||
+ | *[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/Microsoft/BotBuilder Microsoft Bot Builder] | ||
*[https://github.com/gunthercox/ChatterBot ChatterBot] [[Python]]驱动 | *[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/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 | ||
+ | |||
+ | ==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== | ==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] | ||
第39行: | 第57行: | ||
==图集== | ==图集== | ||
<gallery> | <gallery> | ||
+ | image:NLU-NLP-ASR.png|NLU,NLP,ASR | ||
image:drqa.png|DrQA | image:drqa.png|DrQA | ||
image:difference-between-classical-nlp-deep-learning-nlp.png|传统NLP和深度学习NLP | image:difference-between-classical-nlp-deep-learning-nlp.png|传统NLP和深度学习NLP | ||
第44行: | 第63行: | ||
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> | ||
==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] | *[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.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] | ||
第60行: | 第82行: | ||
*[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
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
[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
分享您的观点