Lyapunov filtering of objectivity for Spanish Sentiment Model

Proceedings of the International Joint Conference on Neural Networks

Volumn 2016-October, Issue , 2016, Pages 4474-4481

  Chaturvedi, Iti ^a   Cambria, Erik ^a   Vilares, David ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR MATRIX INEQUALITIES;

CONVOLUTION KERNEL; DEEP NEURAL NETWORKS; HIDDEN LAYERS; LARGE VOCABULARY; MULTIPLE KERNEL LEARNING; VANISHING GRADIENT;

RECURRENT NEURAL NETWORKS;

EID: 85007211651     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2016.7727785     Document Type: Conference Paper

References (34)

1. E. Riloff and J. Wiebe, "Learning extraction patterns for subjective expressions", in Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003, pp. 105-112.
2. D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin, "Learning sentiment-specific word embedding for twitter sentiment classification. " in ACL, 2014, pp. 1555-1565.
3. M. Bonzanini, M. Martinez-Alvarez, and T. Roelleke, "Opinion summarisation through sentence extraction: An investigation with movie reviews", in Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012, pp. 1121-1122.
4. E. Cambria, B. Schuller, B. Liu, H. Wang, and C. Havasi, "Statistical approaches to concept-level sentiment analysis;' IEEE Intelligent Systems, vol. 28, no. 3, pp. 6-9, 2013.
5. E. Cambria, "Affective computing and sentiment analysis;' IEEE Intelligent Systems, vol. 31, no. 2, pp. 102-107, 2016.
6. J. Wiebe and E. Riloff, "Creating subjective and objective sentence classifiers from unannotated texts", in Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing, 2005, pp. 486-497.
7. R. Mihalcea, C. Banea, and J. Wiebe, "Learning multilingual subjective language via cross-lingual projections", in Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, 2007.
8. C. Banea, R. Mihalcea, J. Wiebe, and S. Hassan, "Multilingual subjectivity analysis using machine translation", in Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2008, pp. 127-135.
9. C. Banea, R. Mihalcea, and J. Wiebe, "A bootstrapping method for building subjectivity lexicons for languages with scarce resources." in Proceedings of the Sixth International Conference on Language Resources and Evaluation. European Language Resources Association, 2008.
10. S. Chandar, M. Khapra, B. Ravindran, V. Raykar, and A. Saha, "Multilingual deep learning", in NIPS Deep Learning Workshop, 2013.
11. S. Chandar, S. Lauly, H. Larochelle, M. Khapra, R. Balaraman, Y. Raykar, and A. Saha, "An autoencoder approach to learning bilingual word representations", in Advances in Neural Information Processing Systems 27, 2014, pp. 1853-1861.
12. E. Cambria, A. Hussain, C. Havasi, and C. Eckl, "Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems", ser. Lecture Notes in Computer Science. Berlin Heidelberg: Springer, 2010, vol. 5967, pp. 148-156.
13. E. Cambria and A. Hussain, Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Cham, Switzerland: Springer, 2015.
14. S. Poria, E. Cambria, A. Gelbukh, F. Bisio, and A. Hussain, "Sentiment data flow analysis by means of dynamic linguistic patterns", IEEE Computational Intelligence Magazine, vol. 10, no. 4, pp. 26-36, 2015.
15. F. Sha and F. Pereira, "Shallow parsing with conditional random fields;' in NAACL, 2003, pp. 134-141.
16. C. Fellbaum, Ed., WordNet: an electronic lexical database. MIT Press, 1998.
17. N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A convolutional neural network for modelling sentences;' in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2014, pp. 655-665.
18. R. Collobert and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multi task learning", in Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 160-167.
19. N. Kalchbrenner and P. Blunsom, "Recurrent convolutional neural networks for discourse compositionality", CoRR, vol. 1306-3584, 2013.
20. J. Villena-Roman, J. Garcia-Morera, M. Garcia-Cumbreras, E. MartinezCamara, M. Martin-Valdivia, and L. Urena-Lopez, "Overview of tass 2015", in Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN, 2015.
21. Z. Wang, Y. Liu, and X. Liu, "State estimation for jumping recurrent neural networks with discrete and distributed delays;' Neural Networks, vol. 22, no. I, pp. 41-48, 2009.
22. L. Chaturvedi, E. Cambria, F. Zhu, L. Qiu, and W. K. Ng, "Multilingual subjectivity detection using deep multiple kernel learning;' Proceedings of KDD Wisdom, Sydney, 2015.
23. E. Cambria, J. Fu, F. Bisio, and S. Poria, "AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis", in AAAI, Austin, 2015, pp. 508-514.
24. S. Poria, A. Gelbukh, E. Cambria, A. Hussain, and G.-B. Huang, "EmoSenticSpace: A novel framework for affective common-sense reasoning", Knowledge-Based Systems, vol. 69, pp. 108-123, 2014.
25. E. Cambria, A. Hussain, C. Havasi, and C. Eck:!, "SenticSpace: Visualizing opinions and sentiments in a multi-dimensional vector space;' in Knowledge-Based and Intelligent Information and Engineering Systems, ser. Lecture Notes in Artificial Intelligence, R. Setchi, l. Jordanov, R. Howlett, and L. Jain, Eds. Berlin: Springer, 2010, vol. 6279, pp. 385-393.
26. P. Koehn, Statistical machine translation. Cambridge University Press, 2009.
27. R. Petrov, S. and Das, D. and McDonald, "A universal part-of-speech tagset", arXiv preprint arXiv:1104.2086, 2011.
28. R. McDonald, J. Nivre, and et al., "Universal Dependency Annotation for Multilingual Parsing", in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2013, pp. 92-97.
29. K. Gimpel, N. Schneider, B. O'Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith, "Part-ofspeech tagging for twitter: annotation, features, and experiments", in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers, vol. 2. Association for Computational Linguistics, 2011, pp. 42-47.
30. K. Toutanova and C. D. Manning, "Enriching the knowledge sources used in a maximum entropy part-of-speech tagger", in Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLPNLC-2000), 2000, pp. 63-70.
31. E. Brill and P. Resnik, "A Rule-based Approach to Prepositional Phrase Attachment Disambiguation", in Proc. of COLlNG'94, Kyoto, Japan, 1994, pp. 1198-1204.
32. A. Moro, F. Cecconi, and R. Navigli, "Multilingual word sense disambiguation and entity linking for everybody." in International Semantic Web Conference, M. Horridge, M. Rospocher, and J. van Ossenbruggen, Eds., vol. 1272. CEUR-WS.org, 2014, pp. 25-28.
33. R. Navigli and S. P. Ponzetto, "Babelnet: Building a very large multilingual semantic network;' in Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, 2010, pp. 216-225.
34. D. Vilares, Y. Doval, M. A. Alonso, and C. Gomez-Rodriguez, "Lys at tass 2014: A prototype for extracting and analysing aspects from spanish tweets;' Proceedings of the TASS workshop at SEPLN, 2014.