Natural Language Processing Tasks and Selected References

I've been working on several natural language processing tasks for a long time. One day, I felt like drawing a map of the NLP field where I earn a living. I'm sure I'm not the only person who wants to see at a glance which tasks are in NLP.

I did my best to cover as many as possible tasks in NLP, but admittedly this is far from exhaustive purely due to my lack of knowledge. And selected references are biased towards recent deep learning accomplishments. I expect these serve as a starting point when you're about to dig into the task. I'll keep updating this repo myself, but what I really hope is you collaborate on this work. Don't hesitate to send me a pull request!

Oct. 13, 2017.

by Kyubyong

Reviewed and updated by YJ Choe on Oct. 18, 2017.

Anaphora Resolution

Automated Essay Scoring

Automatic Speech Recognition

Automatic Summarisation

Coreference Resolution

Entity Linking

Grammatical Error Correction

Grapheme To Phoneme Conversion

Humor and Sarcasm Detection

Language Grounding

Language Guessing

Language Identification

Language Modeling

Language Recognition

Lemmatisation

Lip-reading

Machine Translation

Morphological Inflection Generation

Named Entity Disambiguation

Named Entity Recognition

Paraphrase Detection

Paraphrase Generation

Parsing

Part-of-speech Tagging

Pinyin-To-Chinese Conversion

Question Answering

Relationship Extraction

Semantic Role Labeling

Sentence Boundary Disambiguation

Sentiment Analysis

Singing Voice Synthesis

Social Science Applications

Source Separation

Speaker Authentication

Speaker Diarisation

Speaker Recognition

Speech Reading

Speech Recognition

Speech Segmentation

Speech Synthesis

Speech Enhancement

Speech-To-Text

Spoken Term Detection

Stemming

Term Extraction

Text Similarity

Text Simplification

Text-To-Speech

Textual Entailment

Transliteration

Voice Conversion

Voice Recognition

Word Embeddings

Word Prediction

Word Segmentation

Word Sense Disambiguation

— Language Models, Segmentation
— Morphological Analysis, POS Tagging and Sequence Labeling
— Syntactic and Semantic Parsing
— Lexical and Compositional Semantics
— Discourse and Coreference
— Dialogue and Interactive Systems
— Narrative Understanding and Commonsense Reasoning
— Spoken Language Processing
— Text Mining
— Sentiment Analysis and Opinion Mining
— Information Retrieval, Question Answering
— Information Extraction
— Summarization
— Natural Language Generation
— Machine Translation
— Multilinguality and Cross-linguality
— Linguistic Theories and Resources
— Computational Psycholinguistics
— Multimodal and Grounded Language Processing
— Machine Learning for NLP
— Web, Social Media and Computational Social Science
— Ethics and Fairness in NLP
— Other NLP Applications

[转]NLP Tasks的更多相关文章

  1. NLP里面的一些基本概念

    1,corpus 语料库 a computer-readable collection of text or speech 2,utterance 发音 比如下面一句话:I do uh main- m ...

  2. [转] Understanding Convolutional Neural Networks for NLP

    http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ 讲CNN以及其在NLP的应用,非常 ...

  3. Understanding Convolutional Neural Networks for NLP

    When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs ...

  4. [NLP] cs224n-2019 Assignment 1 Exploring Word Vectors

      CS224N Assignment 1: Exploring Word Vectors (25 Points)¶ Welcome to CS224n! Before you start, make ...

  5. CNN for NLP

    卷积神经网络在自然语言处理任务中的应用.参考链接:Understanding Convolutional Neural Networks for NLP(2015.11) Instead of ima ...

  6. 最佳实践:深度学习用于自然语言处理(Deep Learning for NLP Best Practices) - 阅读笔记

    https://www.wxnmh.com/thread-1528249.htm https://www.wxnmh.com/thread-1528251.htm https://www.wxnmh. ...

  7. 基于OpenSeq2Seq的NLP与语音识别混合精度训练

    基于OpenSeq2Seq的NLP与语音识别混合精度训练 Mixed Precision Training for NLP and Speech Recognition with OpenSeq2Se ...

  8. 常用python机器学习库总结

    开始学习Python,之后渐渐成为我学习工作中的第一辅助脚本语言,虽然开发语言是Java,但平时的很多文本数据处理任务都交给了Python.这些年来,接触和使用了很多Python工具包,特别是在文本处 ...

  9. (转) The major advancements in Deep Learning in 2016

    The major advancements in Deep Learning in 2016 Pablo Tue, Dec 6, 2016 in MACHINE LEARNING DEEP LEAR ...

随机推荐

  1. iOS开发-KVC和KVO的理解

    KVC和KVO看起来很专业,其实用起来还是比较简单的,KVC(Key-value coding)可以理解为键值对编码,如果对象的基本类型,那么键值对编码实际上和get,set方法没有区别,如果是属性是 ...

  2. Matplotlib绘图双纵坐标轴设置及控制设置时间格式

    双y轴坐标轴图 今天利用matplotlib绘图,想要完成一个双坐标格式的图. fig=plt.figure(figsize=(20,15)) ax1=fig.add_subplot(111) ax1 ...

  3. mysql开启日志sql语句

    #查看日期情况 #show variables like '%general%'; #开启日志 #SET GLOBAL general_log = 'On'; #指定日志文件 #SET GLOBAL ...

  4. Jsonp 关键字详解及json和jsonp的区别,ajax和jsonp的区别

    为什么要用jsonp? 相信大家对跨域一定不陌生,对同源策略也同样熟悉.什么,你没听过?没关系,既然是深入浅出,那就从头说起. 假如我写了个index页面,页面里有个请求,请求的是一个json数据(不 ...

  5. variable_scope和name_scope差别

    先看代码:   #命名空间函数tf.variable_scope()和tf.name_scope()函数区别于使用       import tensorflow as tf       with t ...

  6. JAVA-Eclipse中web-inf和meta-inf文件夹

    WEB-INF     /WEB-INF/web.xml        你的Web应用程序配置文件,这是一个XML文件,其中描述了 servlet 和其他的应用组件配置及命名规则:  /WEB- IN ...

  7. Sharepoint claim认证的login name

    当SharePoint网站开启了Claims认证后,取回来的user的loginname是一个奇怪的字符串,这个到底是什么意思那? 这篇文章详细解释了: https://blogs.msdn.micr ...

  8. Git直接拉取远程分支

    用Git,一直有个疑惑,可不可以不拉取远程Origin主干,我直接pull一个分支下来 今天想了一下,找到了一个办法 本地分支关联 // 0.新建一个文件夹,然后初始化git git init // ...

  9. CAD2006您没有足够的权限来安装本产品

    在Win10的环境下安装CAD2006,可能会报错"您没有足够的权限来安装本产品". 解决方法是,右键以"兼容性疑难解答"运行 在弹出的对话框中,点击 &quo ...

  10. APUE 3rd

    以下是APUE 3rd edition 的preface,从04年的第二版到现在的第三版,APUE内容有所更新.点击下载. It’s been almost eight years since I fi ...