Abstract
In this work, we report the results of our experiments on the task of distinguishing the semantics of verb-noun collocations in a Spanish corpus. This semantics was represented by four lexical functions of the Meaning-Text Theory. Each lexical function specifies a certain universal semantic concept found in any natural language. Knowledge of collocation and its semantic content is important for natural language processing, as collocation comprises the restrictions on how words can be used together. We experimented with a combination of GloVe word embeddings as a recent and extended algorithm for vector representation of words and a deep neural architecture, in order to recover most of the context of verb-noun collocations in a meaningful way which could discriminate among lexical functions. Our corpus was a collection of 1,131 Excelsior newspaper issues. As our results showed, the proposed deep neural architecture outperformed state-of-the-art supervised learning methods.
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References
Enikeeva, E., Popov, A.: Developing a Russian database of regular semantic relations based on word embeddings. In: Krek, S., Cibej, J., Gorjanc, V., Kosem, I. (eds.) Proceedings of the XVIII EURALEX International Congress: Lexicography in Global Contexts, Ljubljana, Slovenia, pp. 799–809 (2018)
Fonseca, A., Sadat, F., Lareau, F.: Retrieving information from the French lexical network in RDF/OWL format. In: Calzolari, N., et al. (eds.) Proceedings of the 11th International Conference on Language Resources and Evaluation, Miyazaki, Japan (2018)
Gelbukh, A., Kolesnikova, O.: Supervised learning for semantic classification of Spanish collocations. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds.) MCPR 2010. LNCS, vol. 6256, pp. 362–371. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15992-3_38. The complete list of 737 Spanish verb-noun collocations annotated with 36 lexical functions can be accessed at http://148.204.58.221/okolesnikova/index.php?id=lex/ or http://www.gelbukh.com/lexical-functions
Kolesnikova, O., Gelbukh, A.: Exploring the context of lexical functions. In: Batyrshin, I., Martínez-Villaseñor, L., Ponce Espinosa, H.E. (eds.) MICAI 2018. LNCS (LNAI), vol. 11289, pp. 57–69. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04497-8_5
Lux-Pogodalla, V., Polguère, A.: Construction of a French lexical network: methodological issues. In: First International Workshop on Lexical Resources, Ljubljana, Slovenia, pp. 54–61 (2011)
Mel’čuk, I.A.: Lexical functions: A tool for the description of lexical relations in a lexicon. In: Wanner, L. (ed.) Lexical Functions in Lexicography and Natural Language Processing, pp. 37–102. Benjamins Academic Publishers, Amsterdam (1996)
Miller, G.A., Leacock, C., Tengi, R., Bunker, R.T.: A semantic concordance. In: Proceedings of the Workshop on Human Language Technology Association for Computational Linguistics, Stroudsburg, PA, pp. 303–308 (1993)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119. Neural Information Processing Systems Foundation, Inc. (2013)
Polguère A.: Towards a theoretically-motivated general public dictionary of semantic derivations and collocations for French. In: Proceedings of EURALEX 2000, Stuttgart, Germany, pp. 517–527 (2000)
Tutin, A.: Annotating lexical functions in corpora: showing collocations in context. In: Apresjan, Y., Iomdin, L. (eds.) Proceedings of the Second International Conference on the Meaning-Text Model, pp. 498–510. Slavic Culture Languages Publishing House, Moscow (2007)
Vossen, P.: EuroWordNet: a multilingual database of autonomous and language-specific wordnets connected via an inter-lingual-index. Int. J. Lexicography 17(2), 161–173 (2004)
Wanner, L.: Towards automatic fine-grained classification of verb-noun collocations. Nat. Lang. Eng. 10(2), 95–143 (2004)
Wanner, L., Bohnet, B., Giereth, M.: What is beyond collocations? Insights from machine learning experiments. In: Proceedings of the 12th EURALEX International Congress, Turin, Italy, pp. 1071–1084 (2006)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
Yousefi-Azar, M., Hamey, L.: Text summarization using unsupervised deep learning. Expert Syst. Appl. 68, 93–105 (2017)
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The research was done under partial support of Mexican Government: SNI, BEIFI-IPN, and SIP-IPN grants 20196021, 20196437.
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Hernández-Miranda, A., Gelbukh, A., Kolesnikova, O. (2019). Lexical Function Identification Using Word Embeddings and Deep Learning. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_7
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