TextMatching
Methods about Deep Learning for Text Matching
Understanding the Methods in Text Matching Area Including Key-words based Matching Model & Latent Semantic Matching Model. Implement the Classical Methods. The project is written primarily in Python, first published in 2017. Key topics include: deep-learning, deep-matching-model, nlp, text-matching.
For what
Understanding the Methods in Text Matching Area Including Key-words based Matching Model & Latent Semantic Matching Model.
Implement the Classical Methods.
Categories
- tradition model (feature based models)
- Key-words based methods
- tf-idf model
- words common rate model
- find the most important word with adding syntax information
- boosting models
- linear models
- factorization machine
- Key-words based methods
- Semantic deep model
- representation-based models
- DSSM, CDSSM
- interaction-based models
- representation-based models
People in these area
Survey
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Methods & Papers about Semantic Methods
DSSM
<br> Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
<br> CIKM 2013
<br> 词袋模型,基于语义表达的结构, word hash + DNN
<br> 详细解释
<br> 代码
CDSSM
<br> Learning Semantic Representations Using Convolutional Neural Networks for Web Search
<br> WWW 2014, word hash + CNN + DNN
CLSM
<br> A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
<br> CIKM 2014
<br> 基于匹配的结构, word hash + CNN, CLSM和C-DSSM有什么区别呢
DSSM的应用
Modeling Interestingness with Deep Neural Networks
<br> EMNLP 2014
<br> DSSM应用于文本分析，在automatic highlighting和contextual entity search问题上效果好。
<br> 主要有两点贡献：
<br> 1) DSSM + CNN
<br> 2) 不针对相关性，加了一个ranker
ARC-I/ARC-II
Convolutional Neural Network Architectures
for Matching Natural Language Sentences
<br> NIPS 2014
<br> CNN的基于语义表达和基于匹配的两种结构; 增加了门解决句子长度不一致问题
CNTN
<br> Convolutional Neural Tensor Network
Architecture for Community-based Question Answering
<br> IJCAI 2015
<br> (D)CNN+MLP(tensor layer);
<br> 基于语义表达的结构
DeepMatch
<br> A Deep Architecture for Matching Short Texts
<br> NIPS 2013
<br> Reviews
<br> 目的：建模更复杂的匹配关系。最早的基于匹配的结构把。
<br> 结合了localness和hierarchy intrinsic，基于点积的网络不好做的，最大的亮点是用话题模型建立网络吧。
DeepMatch_tree
<br> Syntax-based Deep Matching of Short Texts
Methods & Papers about Key Words Based Methods
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Related talks and books
- Deep Learning for Web Search and
Natural Language Processing - Deep Learning for Information Retrieval(Sigir 2016 Tutorial)
- Semantic Matching in Search (Sigir 2014 Workshop)
- Semantic Matching in Search (Book 2014)
- gensim notebook
Downloads
DSSM/Sent2Vec Release Version
<br> MSRA发布的Sent2Vec发行版
Datasets
- Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (fb.ai/babi)
- Teaching Machines to Read and Comprehend (github.com/deepmind/rc-data)
- One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling (github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark)
- The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems (cs.mcgill.ca/~jpineau/datasets/ubuntu-corpus-1.0)
- Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books (BookCorpus)
- Every publicly available Reddit comment, for research.
- Stack Exchange Data Dump
- Europarl: A Parallel Corpus for Statistical Machine Translation (www.statmt.org/europarl/)
- RTE Knowledge Resources
- Kaggle Quora Question Pairs
Competition
Pretrained Models
Important Online Courses
- Stanford CS224d Deep Learning for Natural Language Processing
- Stanford CS20SI Tensorflow for Deep Learning Research
- Stanford CS231n Convolutional Neural Networks for Visual Recognition
References
https://github.com/robertsdionne/neural-network-papers/blob/master/README.md
Contributors
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