Conference article

Lexical Modeling for Natural Language Processing

Alexander Popov
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria

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Published in: Selected papers from the CLARIN Annual Conference 2018, Pisa, 8-10 October 2018

Linköping Electronic Conference Proceedings 159:16, p. 152-165

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Published: 2019-05-28

ISBN: 978-91-7685-034-3

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

This paper describes a multi-component research project on the computational lexicon, the results of which will be used and built upon in work within the CLARIN infrastructure to be developed by the Bulgarian national consortium. PrincetonWordNet is used as the primary lexicographic resource for producing machine-oriented models of meaning. Its dictionary and semantic network are used to build knowledge graphs, which are then enriched with additional semantic and syntactic relations extracted from various other sources. Experimental results demonstrate that this enrichment leads to more accurate lexical analysis. The same graph models are used to create distributed semantic models (or ”embeddings”), which perform very competitively on standard word similarity and relatedness tasks. The paper discusses how such vector models of the lexicon can be used as input features to neural network systems for word sense disambiguation. Several neural architectures are discussed, including two multi-task architectures, which are trained to reflect more accurately the polyvalent nature of lexical items. Thus, the paper provides a faceted view of the computational lexicon, in which separate aspects of it are modeled in different ways, relying on different theoretical and data sources, and are used to different purposes.

Keywords

Lexical modeling, WordNet, Word sense disambiguation, Neural networks, Word embeddings, Knowledge graphs

References

Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pas¸ca, M., & Soroa, A. 2009. A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: 19-27.

Agirre, E. and Soroa, A. 2009. Personalizing PageRank for Word Sense Disambiguation. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics: 33-41.

Agirre, E., de Lacalle, O.L. and Soroa, A. 2018. The Risk of Sub-optimal Use of Open Source NLP Software: UKB is Inadvertently State-of-the-art in Knowledge-based WSD. arXiv preprint arXiv:1805.04277.

Alonso, H.M. and Plank, B. 2016. When is Multitask Learning Effective? Semantic Sequence Prediction Under Varying Data Conditions. arXiv preprint arXiv:1612.02251.

Bingel, J. and Søgaard, A. 2017. Identifying Beneficial Task Relations for Multi-task Learning in Deep Neural Networks. arXiv preprint arXiv:1702.08303.

Bonial, C., Stowe, K. and Palmer, M. 2013. Renewing and Revising SemLink. In Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, Terminologies and Other Language Data: 9-17.

Camacho-Collados, J., Pilehvar, M.T. and Navigli, R. 2015. NASARI: a Novel Approach to a Semantically-aware Representation of Items. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: 567-577.

De Lacalle, M.L., Laparra, E. and Rigau, G. 2014. Predicate Matrix: Extending SemLink through WordNet Mappings. In LREC: 903-909.

Faruqui, M., Dodge, J., Jauhar, S.K., Dyer, C., Hovy, E. and Smith, N.A. 2014. Retrofitting Word Vectors to Semantic Lexicons. arXiv preprint arXiv:1411.4166.

Fellbaum, Christiane. 1998. Wordnet. Wiley Online Library.

Goikoetxea, J., Soroa, A. and Agirre, E. 2015. Random Walks and Neural Network Language Models on Knowledge Bases. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: 1434-1439.

Harabagiu, S. M. and Moldovan, D. I. 2000. Enriching the WordNet Taxonomy with Contextual Knowledge Acquired from Text. Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language, 301-333.

Hill, F., Reichart, R., Korhonen, A. (2015). Simlex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation. Computational Linguistics, 41 (4): 665-695.

Huang, Z., Xu, W. and Yu, K.. 2015. Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv preprint arXiv:1508.01991.

Iacobacci, I., Pilehvar, M.T. and Navigli, R. 2015. Sensembed: Learning Sense Embeddings for Word and Relational Similarity. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers): 95-105.

Iacobacci, I., Pilehvar, M.T. and Navigli, R. 2016. Embeddings for Word Sense Disambiguation: An Evaluation Study. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers): 897-907.

Johansson, R. and Pina, L.N. 2015. Embedding a Semantic Network in a Word Space. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: 1428-1433.

Kågebäck, M. and Salomonsson, H. 2016. Word Sense Disambiguation Using a Bidirectional lstm. arXiv preprint arXiv:1606.03568.

Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A. and Fidler, S. 2015. Skip-thought Vectors. In Advances in Neural Information Processing Systems: 3294-3302.

Levin, Beth. 1993. English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press.

Levy, O. and Goldberg, Y. 2014. Dependency-based Word Embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers): 302-308.

Mancini, M., Camacho-Collados, J., Iacobacci, I. and Navigli, R. 2016. Embedding Words and Senses Together via Joint Knowledge-enhanced Training. arXiv preprint arXiv:1612.02703.

Melamud, O., Goldberger, J. and Dagan, I. 2016. context2vec: Learning Generic Context Embedding with Bidirectional LSTM. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning: 51-61.

Mihalcea, R. and Moldovan, D. I. 2001. eXtendedWordNet: Progress Report. In Proceedings of NAACLWorkshop on WordNet and Other Lexical Resources: 95100.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J. 2013. Distributed Representations of Words and Phrases and Their Compositionality. In Advances in Neural Information Processing Systems: 3111-3119.

Mikolov, T., Chen, K., Corrado, G. and Dean, J. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.

Miller, G.A., Leacock, C., Tengi, R. and Bunker, R.T. 1993. A Semantic Concordance. In Proceedings of the Workshop on Human Language Technology, Association for Computational Linguistics: 303-308.

Navigli, R. 2009. Word Sense Disambiguation: A Survey. ACM Computing Surveys (CSUR), 41(2): p.10.

Nguyen, D.Q., Dras, M. and Johnson, M. 2017. A Novel Neural Network Model for Joint POS Tagging and Graphbased Dependency Parsing. arXiv preprint arXiv:1705.05952.

Palmer, M. 2009. Semlink: Linking Propbank, Verbnet and FrameNet. In Proceedings of the Generative Lexicon Conference, Pisa, Italy: GenLex-09: 9-15.

Pennington, J., Socher, R. and Manning, C. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP): 1532-1543.

Plank, B., Sgaard, A. and Goldberg, Y. 2016. Multilingual Part-of-speech Tagging with Bidirectional Long Shortterm Memory Models and Auxiliary Loss. arXiv preprint arXiv:1604.05529.

Popov, A. 2017. Word Sense Disambiguation with Recurrent Neural Networks. In Proceedings of the Student Research Workshop associated with RANLP: 25-34.

Raganato, A., Camacho-Collados, J. and Navigli, R. 2017. Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers: 99-110.

Raganato, A., Bovi, C.D. and Navigli, R. 2017. Neural Sequence Learning Models forWord Sense Disambiguation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: 1156-1167.

Rothe, S. and Schütze, H. 2015. Autoextend: Extending Word Embeddings to Embeddings for Synsets and Lexemes. arXiv preprint arXiv:1507.01127.

Ruder, S., 2017. An Overview of Multi-task Learning in Deep neural Networks. arXiv preprint arXiv:1706.05098.

Schuler, K. K. 2005. VerbNet: A Broad-coverage, Comprehensive Verb Lexicon.

Simov, K., Osenova, P., & Popov, A. 2016. Using Context Information for Knowledge-based Word Sense Disambiguation. In International Conference on Artificial Intelligence: Methodology, Systems, and Applications: 130-139. Springer, Cham.

Simov, K., Popov, A., & Osenova, P. 2015. Improving Word Sense Disambiguation with Linguistic Knowledge from a Sense Annotated Treebank. In Proceedings of the International Conference Recent Advances in Natural Language Processing: 596-603.

Wang, P., Qian, Y., Soong, F.K., He, L. and Zhao, H. 2015. Part-of-speech Tagging with Bidirectional Long Shortterm Memory Recurrent Neural Network. arXiv preprint arXiv:1510.06168.

Wang, P., Qian, Y., Soong, F.K., He, L. and Zhao, H. 2015. A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding. arXiv preprint arXiv:1511.00215.

Zhong, Z. and Ng, H.T. 2010. It Makes Sense: AWide-coverageWord Sense Disambiguation System for Free Text. In Proceedings of the ACL 2010 System Demonstrations, Association for Computational Linguistics: 78-83.

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