michelangelo-42 / nlp-paper-summaries

Summaries of some NLP research papers

NLP-Paper-Summaries

Summaries of some NLP research papers

• The summaries are originally a part for UCLA CS249 (Professor Cho, Spring 2019).
• Note: Just for reference, and plagiarism will result in the risk of penalty.

Paper list:

1. Yoshua Bengio, et al.: A Neural Probabilistic Language Model, J. of Machine Learning Research, 2003.
2. Tomas Mikolov, et al.: Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.
3. Jeffrey Pennington, et al.: GloVe: Global Vectors for Word Representation, 2014.
4. Quoc V. Le and Tomas Mikolov: Distributed Representations of Sentences and Documents, 2014.
5. Joshua Goodman: A bit of progress in language modeling, MSR Technical Report, 2001.
6. Yee Whye Teh: A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, COLING/ACL 2006.
7. Kamal Nigam, et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999.
8. Adam Berger, Stephen Della Pietra, Vincent Pietra: A Maximum Entropy Approach to Natural Language Processing, J of Computational Linguistics 1996.
9. Michael Collins: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, EMNLP 2002.
10. John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, ICML 2001.
11. Ryan McDonald et al.: Non-Projective Dependency Parsing using Spanning-Tree Algorithms, EMNLP 2005.
12. Ronan Collobert et al.: Natural Language Processing (almost) from Scratch, J. of Machine Learning Research, 2011.
13. Danqi Chen, Christopher D. Manning: A Fast and Accurate Dependency Parser using Neural Networks, EMNLP 2014.
14. Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio: Neural Machine Translation by Jointly Learning to Align and Translate, ICLR 2015.