Awesome Resource

A personal curated list of resources for natrual language processing.

https://pdfs.semanticscholar.org/0c6f/38b702f87758d49ec7a9824d05bd32b53979.pdf

Papers

Representation

• Kiros R, Zhu Y, Salakhutdinov RR, Zemel R, Urtasun R, Torralba A, Fidler S. Skip-thought vectors. InAdvances in neural information processing systems 2015 (pp. 3294-3302).PDF
• Pennington J, Socher R, Manning CD. Glove: Global Vectors for Word Representation. InEMNLP 2014 Oct 25 (Vol. 14, pp. 1532-43). PDF
• Le QV, Mikolov T. Distributed Representations of Sentences and Documents. InICML 2014 Jun 21 (Vol. 14, pp. 1188-1196). PDF
• Rong X. word2vec parameter learning explained. arXiv preprint arXiv:1411.2738. 2014 Nov 11. PDF
• Trask A, Michalak P, Liu J. sense2vec-A fast and accurate method for word sense disambiguation in neural word embeddings. arXiv preprint arXiv:1511.06388. 2015 Nov 19. PDF
• Nalisnick E, Ravi S. Infinite dimensional word embeddings. arXiv preprint arXiv:1511.05392. 2015 Nov 17. PDF
• Bartunov S, Kondrashkin D, Osokin A, Vetrov D. Breaking Sticks and Ambiguities with Adaptive Skip-gram. arXiv preprint arXiv:1502.07257. 2015 Feb 25.PDF

Language Model

• Kim Y, Jernite Y, Sontag D, Rush AM. Character-aware neural language models. arXiv preprint arXiv:1508.06615. 2015 Aug 26. ([PDF] | [CODE])
• Sutskever I, Martens J, Hinton GE. Generating text with recurrent neural networks. InProceedings of the 28th International Conference on Machine Learning (ICML-11) 2011 (pp. 1017-1024).([PDF])
• Li J, Ouazzane K, Kazemian HB, Afzal MS. Neural network approaches for noisy language modeling. IEEE transactions on neural networks and learning systems. 2013 Nov;24(11):1773-84.
• Chien JT, Ku YC. Bayesian Recurrent Neural Network for Language Modeling. IEEE transactions on neural networks and learning systems. 2016 Feb;27(2):361-74.
• Tomas M, Geoffrey Z. Context Dependent Recurrent Neural Network Language Model. Microsoft Research Technical Report MSR-TR-2012-92. 2012 Jul. PDF
• Sundermeyer M, Schlüter R, Ney H. LSTM Neural Networks for Language Modeling. InInterspeech 2012 Sep (pp. 194-197). PDF
• Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. journal of machine learning research. 2003;3(Feb):1137-55.PDF

Sentiment Analysis

• Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. InProceedings of the conference on empirical methods in natural language processing (EMNLP) 2013 Oct 18 (Vol. 1631, p. 1642). ([PDF])
• dos Santos CN, Gatti M. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. InCOLING 2014 (pp. 69-78). PDF
• Tang D, Qin B, Liu T.** Aspect Level Sentiment Classification with Deep Memory Network.** arXiv preprint arXiv:1605.08900. 2016 May 28. PDF
• Li X, Pang J, Mo B, Rao Y, Wang FL. Deep Neural Network for Short-Text Sentiment Classification. InInternational Conference on Database Systems for Advanced Applications 2016 Apr 16 (pp. 168-175). Springer International Publishing.
• Severyn A, Moschitti A. UNITN: Training deep convolutional neural network for Twitter sentiment classification. InProceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Association for Computational Linguistics, Denver, Colorado 2015 Jun 4 (pp. 464-469). PDF
• Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. InProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing 2015 (pp. 1422-1432).PDF
• Tang D, Qin B, Liu T. Learning semantic representations of users and products for document level sentiment classification. InProc. ACL 2015.PDF
• Severyn A, Moschitti A. Twitter sentiment analysis with deep convolutional neural networks. InProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval 2015 Aug 9 (pp. 959-962). ACM.PDF
• Poria S, Cambria E, Gelbukh A. Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. InProceedings of EMNLP 2015 (pp. 2539-2544). PDF
• Li C, Xu B, Wu G, He S, Tian G, Hao H. Recursive Deep Learning for Sentiment Analysis over Social Data. InProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02 2014 Aug 11 (pp. 180-185). IEEE Computer Society.
• Pham SB. Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features. ACL 2014. 2014 Jun 27:128. PDF
• Balahur A, Steinberger R, Kabadjov M, Zavarella V, Van Der Goot E, Halkia M, Pouliquen B, Belyaeva J. Sentiment analysis in the news. arXiv preprint arXiv:1309.6202. 2013 Sep 24. PDF
• Gonçalves P, Araújo M, Benevenuto F, Cha M. Comparing and combining sentiment analysis methods. InProceedings of the first ACM conference on Online social networks 2013 Oct 7 (pp. 27-38). ACM.z PDF
• Liu B, Zhang L. A survey of opinion mining and sentiment analysis. InMining text data 2012 (pp. 415-463). Springer US. PDF
• Zhou S, Chen Q, Wang X. Active deep learning method for semi-supervised sentiment classification. Neurocomputing. 2013 Nov 23;120:536-46.
• Tang D, Wei F, Qin B, Liu T, Zhou M. Coooolll: A deep learning system for Twitter sentiment classification. InProceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) 2014 Aug 23 (pp. 208-212).PDF
• Zharmagambetov AS, Pak AA. Sentiment analysis of a document using deep learning approach and decision trees. In2015 Twelve International Conference on Electronics Computer and Computation (ICECCO) 2015 Sep 27 (pp. 1-4). IEEE.
• Zhang X, LeCun Y. Text understanding from scratch. arXiv preprint arXiv:1502.01710. 2015 Feb 5. PDF
• Wang X, Liu Y, Sun C, Wang B, Wang X. Predicting polarities of tweets by composing word embeddings with long short-term memory. InProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing 2015 (Vol. 1, pp. 1343-1353).PDF
• Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. InAdvances in Neural Information Processing Systems 2015 (pp. 649-657).PDF
• Joulin A, Grave E, Bojanowski P, Mikolov T. Bag of Tricks for Efficient Text Classification. arXiv preprint arXiv:1607.01759. 2016 Jul 6. PDF
• Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188. 2014 Apr 8.PDF
• A brief review on sentiment analysis. LINK

Dataset

Sentiment

• https://www.w3.org/community/sentiment/wiki/Datasets

• Stanford Deep Moving LINK

• SAR14 LINK. An independent score-associated dataset of 233600 movie reviews.

• http://help.sentiment140.com/for-students/

• Twitter Sentiment Corpus LINK. It consists of 5513 hand-classified tweets. Each tweet was classified with respect to one of four different topics.

• UMICH SI650 - Sentiment Classification [LINK]. Training data: 7086 lines. Test data: 33052 lines, each contains one sentence.

• Sentiment Labelled Sentences Data Set LINK 3000 items. This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015.

• Twitter Data set for Arabic Sentiment Analysis Data Set LINK. - By using a tweet crawler, we collect 2000 labelled tweets (1000 positive tweets and 1000 negative ones) on various topics such as: politics and arts. These tweets include opinions written in both

• Word Association Lexicons: Capturing word-emotion, word-sentiment, and word-colour associations LINK