Skip to content

Latest commit

 

History

History

natural-language-processing

Folders and files

NameName
Last commit message
Last commit date

parent directory

..

meaning

meaning

 
 

A-Latent-Semantic-Model-with-Convolutional-Pooling-Structure-for-Information-Retrieval.ipynb

A-Latent-Semantic-Model-with-Convolutional-Pooling-Structure-for-Information-Retrieval.ipynb

 
 

A-Neural-Probabilistic-Language-Model.ipynb

A-Neural-Probabilistic-Language-Model.ipynb

 
 

Analysis-of-Wikipedia-based-Corpora-for-Question-Answering.ipynb

Analysis-of-Wikipedia-based-Corpora-for-Question-Answering.ipynb

 
 

Deep-Learning-for-Answer-Sentence-Selection.ipynb

Deep-Learning-for-Answer-Sentence-Selection.ipynb

 
 

Distributed-Representations-of-Sentences-and-Documents.ipynb

Distributed-Representations-of-Sentences-and-Documents.ipynb

 
 

Distributed-Representations-of-Words-and-Phrases-and-their-Compositionality.ipynb

Distributed-Representations-of-Words-and-Phrases-and-their-Compositionality.ipynb

 
 

Document-Embedding-with-Paragraph-Vectors.ipynb

Document-Embedding-with-Paragraph-Vectors.ipynb

 
 

Efficient-Estimation-of-Word-Representations-in-Vector-Space.ipynb

Efficient-Estimation-of-Word-Representations-in-Vector-Space.ipynb

 
 

Googles-Neural-Machine-Translation-System-Bridging-the-Gap-between-Human-and-Machine-Translation.ipynb

Googles-Neural-Machine-Translation-System-Bridging-the-Gap-between-Human-and-Machine-Translation.ipynb

 
 

Learning-Continuous-Phrase-Representations-for-Translation-Modeling.ipynb

Learning-Continuous-Phrase-Representations-for-Translation-Modeling.ipynb

 
 

Learning-Deep-Structured-Semantic-Models-for-Web-Search-using-Clickthrough-Data.ipynb

Learning-Deep-Structured-Semantic-Models-for-Web-Search-using-Clickthrough-Data.ipynb

 
 

Learning-Phrase-Representations-using-RNN-Encoder–Decoder-for-Statistical-Machine-Translation.ipynb

Learning-Phrase-Representations-using-RNN-Encoder–Decoder-for-Statistical-Machine-Translation.ipynb

 
 

Learning-Semantic-Representations-Using-Convolutional-Neural-Networks-for-Web-Search.ipynb

Learning-Semantic-Representations-Using-Convolutional-Neural-Networks-for-Web-Search.ipynb

 
 

Learning-to-Answer-Questions-from-Wikipedia-Infoboxes.ipynb

Learning-to-Answer-Questions-from-Wikipedia-Infoboxes.ipynb

 
 

Linguistic-Regularities-in-Continuous-Space-Word-Representations.ipynb

Linguistic-Regularities-in-Continuous-Space-Word-Representations.ipynb

 
 

MULTI-TASK-SEQUENCE-TO-SEQUENCE-LEARNING.ipynb

MULTI-TASK-SEQUENCE-TO-SEQUENCE-LEARNING.ipynb

 
 

Modeling-Interestingness-with-Deep-Neural-Networks.ipynb

Modeling-Interestingness-with-Deep-Neural-Networks.ipynb

 
 

Natural-Language-Processing-almost-from-Scratch.ipynb

Natural-Language-Processing-almost-from-Scratch.ipynb

 
 

Note-on-Word-Representation.ipynb

Note-on-Word-Representation.ipynb

 
 

README.md

README.md

 
 

Recurrent-neural-network-based-language-model.ipynb

Recurrent-neural-network-based-language-model.ipynb

 
 

SQuAD.ipynb

SQuAD.ipynb

 
 

SelQA.ipynb

SelQA.ipynb

 
 

Semantic-Parsing-for-Single-Relation-Question-Answering.ipynb

Semantic-Parsing-for-Single-Relation-Question-Answering.ipynb

 
 

Semantic-Parsing-via-Staged-Query-Graph-Generation-Question-Answering-with-Knowledge-Base.ipynb

Semantic-Parsing-via-Staged-Query-Graph-Generation-Question-Answering-with-Knowledge-Base.ipynb

 
 

Sequence-to-Sequence-Learning-with-Neural-Networks.ipynb

Sequence-to-Sequence-Learning-with-Neural-Networks.ipynb

 
 

Skip-Thought-Vectors.ipynb

Skip-Thought-Vectors.ipynb

 
 

WIKIQA.ipynb

WIKIQA.ipynb

 
 

text_clustering.ipynb

text_clustering.ipynb

 
 

topic_model.ipynb

topic_model.ipynb

 
 

word2vecExplained.ipynb

word2vecExplained.ipynb

 
 

README.md

自然语言处理

  1. Natural Language Processing,Jacob Eisenstein, Jun 2018
  2. 文本聚类
    • 词袋模型与文本向量化
    • 相似度：向量间距离
    • 聚类KMeans

Corpus

  1. THE PENN TREEBANK-AN OVERVIEW, AnnTaylor, 19951.
  2. The Proposition Bank: An Annotated Corpus of Semantic Roles, Martha Palmer, 2003

Meaning

  1. 语义角色标注 Semantic Role Labeling(SRL)
  2. Abstract Meaning Representation for Sembanking,Laura Banarescu, 2013
  3. 依存文法分析

Topic Model

  1. 基于主题模型比较相似度
    • 构建主题模型
    • 在主题空间比较相似度

Neural Probabilistic Language Model

  1. A Neural Probabilistic Language Model,Yoshua Bengio,Réjean Ducharme,Pascal Vincent,Christian Jauvin,Journal of Machine Learning Research 3 (2003)
  2. Natural Language Processing (almost) from Scratch,Ronan Collobert,Jason Weston,2 Mar 2011

Embedding/Continuous Space Representations

  1. Recurrent neural network based language model,Tomas Mikolov,INTERSPEECH 2010
  2. Linguistic Regularities in Continuous Space Word Representations,Tomas Mikolov,Wen-tau Yih,Geoffrey Zweig,NAACL-HLT 2013
  3. Efficient Estimation of Word Representations in Vector Space,Tomas Mikolov,Kai Chen,Greg Corrado,Jeffrey Dean,7 Sep 2013
  4. Distributed Representations of Words and Phrases and their Compositionality,Tomas Mikolov,Ilya Sutskever,Kai Chen,Greg Corrado,Jeffrey Dean,16 OCT 2013
  5. Distributed Representations of Sentences and Documents,Quoc Le,Tomas Mikolov,22 May 2014
  6. Document Embedding with Paragraph Vectors,Andrew M. Dai,Christopher Olah,Quoc V. Le,29 Jul 2015
  7. Skip-Thought Vectors,Ryan Kiros,Yukun Zhu,Ruslan Salakhutdinov,Richard S. Zemel,Antonio Torralba, Raquel Urtasun, Sanja Fidler,2015

Deep Structured Semantic Models(DSSM)

  1. Learning Deep Structured Semantic Models for Web Search using Clickthrough Data,Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng,Alex Acero, Larry Heck,CIKM’13, Oct. 27 2013
  2. Learning Semantic Representations Using Convolutional Neural Networks for Web Search,Yelong Shen,Xiaodong He,Jianfeng Gao,Li Deng,Grégoire Mesnil,WWW’14 Companion, April 7–11, 2014
  3. Semantic Parsing for Single-Relation Question Answering,Wen-tau Yih,Xiaodong He,Christopher Meek,2014
  4. Modeling Interestingness with Deep Neural Networks,Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng,2014
  5. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval,Yelong Shen,Xiaodong He,Jianfeng Gao,Li Deng,Grégoire Mesnil,CIKM’14, November 03 2014
  6. Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base,Wen-tau Yih,Ming-Wei Chang,Xiaodong He,Jianfeng Gao,2015

Translation

  1. Learning Continuous Phrase Representations for Translation Modeling,Jianfeng Gao,Xiaodong He,Wen-tau Yih,Li Deng,2013
  2. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation,Kyunghyun Cho,Yoshua Bengio, 3 Sep 2014
  3. Sequence to Sequence Learning with Neural Networks,Ilya Sutskever,Oriol Vinyals,Quoc V. Le,14 Dec 2014
  4. MULTI-TASK SEQUENCE TO SEQUENCE LEARNING,Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser,ICLR 2016
  5. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Jeffrey Dean,8 Oct 2016

Natural Language Understanding(NLU)

  1. Deep Learning for Answer Sentence Selection,Lei Yu,Karl Moritz Hermann,Phil Blunsom,Stephen Pulman,4 Dec 2014
  2. WIKIQA: A Challenge Dataset for Open-Domain Question Answering,Yi Yang,Wen-tau Yih,Christopher Meek,17 Sep 2015
  3. SQuAD: 100,000+ Questions for Machine Comprehension of Text,Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang,11 Oct 2016
  4. SelQA: A New Benchmark for Selection-based Question Answering,Tomasz Jurczyk,Michael Zhai,Jinho D. Choi,28 Oct 2016
  5. Learning to Answer Questions from Wikipedia Infoboxes,Alvaro Morales,Varot Premtoon,Boris Katz,1 Nov 2016
  6. Analysis of Wikipedia-based Corpora for Question Answering,Tomasz Jurczyk, Amit Deshmane, Jinho Choi,1 Jan 2018