wesley7137/GoDPMD · Datasets at Hugging Face

text stringlengths 0 9.36M
File: README.md
Contents:
# 100 Must-Read NLP Papers

This is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field should probably know about and read. This list is compiled by [Masato Hagiwara](http://masatohagiwara.net/). I welcome any feedback on this list.

This list is originally based on the answers for a Quora question I posted years ago: [What are the most important research papers which all NLP students should definitely read?](https://www.quora.com/What-are-the-most-important-research-papers-which-all-NLP-students-should-definitely-read). I thank all the people who contributed to the original post.

This list is far from complete or objective, and is evolving, as important papers are being published year after year. Please let me know via [pull requests](https://github.com/mhagiwara/100-nlp-papers/pulls) and [issues](https://github.com/mhagiwara/100-nlp-papers/issues) if anything is missing.

A paper doesn't have to be a peer-reviewed conference/journal paper to appear here. We also include tutorial/survey-style papers and blog posts that are often easier to understand than the original papers.

## Machine Learning

* Avrim Blum and Tom Mitchell: Combining Labeled and Unlabeled Data with Co-Training, 1998.

* John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, ICML 2001.

* Charles Sutton, Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning.

* Kamal Nigam, et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999.

* Kevin Knight: Bayesian Inference with Tears, 2009.

* Marco Tulio Ribeiro et al.: "Why Should I Trust You?": Explaining the Predictions of Any Classifier, KDD 2016.

* Marco Tulio Ribeiro et al.: [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList](https://www.aclweb.org/anthology/2020.acl-main.442/), ACL 2020.

## Neural Models

* Richard Socher, et al.: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, NIPS 2011.

* Ronan Collobert et al.: Natural Language Processing (almost) from Scratch, J. of Machine Learning Research, 2011.

* Richard Socher, et al.: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, EMNLP 2013.

* Xiang Zhang, Junbo Zhao, and Yann LeCun: Character-level Convolutional Networks for Text Classification, NIPS 2015.

* Yoon Kim: Convolutional Neural Networks for Sentence Classification, 2014.

* Christopher Olah: Understanding LSTM Networks, 2015.

* Matthew E. Peters, et al.: Deep contextualized word representations, 2018.

* Jacob Devlin, et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.

* Yihan Liu et al. [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692), 2020.

## Clustering & Word/Sentence Embeddings

* Peter F Brown, et al.: Class-Based n-gram Models of Natural Language, 1992.

* Tomas Mikolov, et al.: Efficient Estimation of Word Representations in Vector Space, 2013.

* Tomas Mikolov, et al.: Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.

* Quoc V. Le and Tomas Mikolov: Distributed Representations of Sentences and Documents, 2014.

* Jeffrey Pennington, et al.: GloVe: Global Vectors for Word Representation, 2014.

* Ryan Kiros, et al.: Skip-Thought Vectors, 2015.

* Piotr Bojanowski, et al.: Enriching Word Vectors with Subword Information, 2017.

* Daniel Cer et al.: [Universal Sentence Encoder](https://arxiv.org/abs/1803.11175), 2018.

## Topic Models

* Thomas Hofmann: Probabilistic Latent Semantic Indexing, SIGIR 1999.

* David Blei, Andrew Y. Ng, and Michael I. Jordan: Latent Dirichlet Allocation, J. Machine Learning Research, 2003.

## Language Modeling

* Joshua Goodman: A bit of progress in language modeling, MSR Technical Report, 2001.

* Stanley F. Chen and Joshua Goodman: An Empirical Study of Smoothing Techniques for Language Modeling, ACL 2006.

* Yee Whye Teh: A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, COLING/ACL 2006.

* Yee Whye Teh: A Bayesian interpretation of Interpolated Kneser-Ney, 2006.

* Yoshua Bengio, et al.: A Neural Probabilistic Language Model, J. of Machine Learning Research, 2003.

* Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks, 2015.

* Yoon Kim, et al.: Character-Aware Neural Language Models, 2015.

* Alec Radford, et al.: [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), 2018.

## Segmentation, Tagging, Parsing

* Donald Hindle and Mats Rooth. Structural Ambiguity and Lexical Relations, Computational Linguistics, 1993.

* Adwait Ratnaparkhi: A Maximum Entropy Model for Part-Of-Speech Tagging, EMNLP 1996.

* Eugene Charniak: A Maximum-Entropy-Inspired Parser, NAACL 2000.

* Michael Collins: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, EMNLP 2002.

File: README.md

Contents:

# 100 Must-Read NLP Papers

This is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field should probably know about and read. This list is compiled by [Masato Hagiwara](http://masatohagiwara.net/). I welcome any feedback on this list.

This list is originally based on the answers for a Quora question I posted years ago: [What are the most important research papers which all NLP students should definitely read?](https://www.quora.com/What-are-the-most-important-research-papers-which-all-NLP-students-should-definitely-read). I thank all the people who contributed to the original post.

This list is far from complete or objective, and is evolving, as important papers are being published year after year. Please let me know via [pull requests](https://github.com/mhagiwara/100-nlp-papers/pulls) and [issues](https://github.com/mhagiwara/100-nlp-papers/issues) if anything is missing.

A paper doesn't have to be a peer-reviewed conference/journal paper to appear here. We also include tutorial/survey-style papers and blog posts that are often easier to understand than the original papers.

## Machine Learning

* Avrim Blum and Tom Mitchell: Combining Labeled and Unlabeled Data with Co-Training, 1998.

* John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, ICML 2001.

* Charles Sutton, Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning.

* Kamal Nigam, et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999.

* Kevin Knight: Bayesian Inference with Tears, 2009.

* Marco Tulio Ribeiro et al.: "Why Should I Trust You?": Explaining the Predictions of Any Classifier, KDD 2016.

* Marco Tulio Ribeiro et al.: [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList](https://www.aclweb.org/anthology/2020.acl-main.442/), ACL 2020.

## Neural Models

* Richard Socher, et al.: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, NIPS 2011.

* Ronan Collobert et al.: Natural Language Processing (almost) from Scratch, J. of Machine Learning Research, 2011.

* Richard Socher, et al.: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, EMNLP 2013.

* Xiang Zhang, Junbo Zhao, and Yann LeCun: Character-level Convolutional Networks for Text Classification, NIPS 2015.

* Yoon Kim: Convolutional Neural Networks for Sentence Classification, 2014.

* Christopher Olah: Understanding LSTM Networks, 2015.

* Matthew E. Peters, et al.: Deep contextualized word representations, 2018.

* Jacob Devlin, et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.

* Yihan Liu et al. [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692), 2020.

## Clustering & Word/Sentence Embeddings

* Peter F Brown, et al.: Class-Based n-gram Models of Natural Language, 1992.

* Tomas Mikolov, et al.: Efficient Estimation of Word Representations in Vector Space, 2013.

* Tomas Mikolov, et al.: Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.

* Quoc V. Le and Tomas Mikolov: Distributed Representations of Sentences and Documents, 2014.

* Jeffrey Pennington, et al.: GloVe: Global Vectors for Word Representation, 2014.

* Ryan Kiros, et al.: Skip-Thought Vectors, 2015.

* Piotr Bojanowski, et al.: Enriching Word Vectors with Subword Information, 2017.

* Daniel Cer et al.: [Universal Sentence Encoder](https://arxiv.org/abs/1803.11175), 2018.

## Topic Models

* Thomas Hofmann: Probabilistic Latent Semantic Indexing, SIGIR 1999.

* David Blei, Andrew Y. Ng, and Michael I. Jordan: Latent Dirichlet Allocation, J. Machine Learning Research, 2003.

## Language Modeling

* Joshua Goodman: A bit of progress in language modeling, MSR Technical Report, 2001.

* Stanley F. Chen and Joshua Goodman: An Empirical Study of Smoothing Techniques for Language Modeling, ACL 2006.

* Yee Whye Teh: A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, COLING/ACL 2006.

* Yee Whye Teh: A Bayesian interpretation of Interpolated Kneser-Ney, 2006.

* Yoshua Bengio, et al.: A Neural Probabilistic Language Model, J. of Machine Learning Research, 2003.

* Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks, 2015.

* Yoon Kim, et al.: Character-Aware Neural Language Models, 2015.

* Alec Radford, et al.: [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), 2018.

## Segmentation, Tagging, Parsing

* Donald Hindle and Mats Rooth. Structural Ambiguity and Lexical Relations, Computational Linguistics, 1993.

* Adwait Ratnaparkhi: A Maximum Entropy Model for Part-Of-Speech Tagging, EMNLP 1996.

* Eugene Charniak: A Maximum-Entropy-Inspired Parser, NAACL 2000.

* Michael Collins: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, EMNLP 2002.