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International Conference on Intelligent Text Processing and Computational Linguistics

CICLing 2016: Computational Linguistics and Intelligent Text Processing pp 115–126Cite as

Using Embedding Models for Lexical Categorization in Morphologically Rich Languages

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

Abstract

Neural-network-based semantic embedding models are relatively new but popular tools in the field of natural language processing. It has been shown that continuous embedding vectors assigned to words provide an adequate representation of their meaning in the case of English. However, morphologically rich languages have not yet been the subject of experiments with these embedding models. In this paper, we investigate the performance of embedding models for Hungarian, trained on corpora with different levels of preprocessing. The models are evaluated on various lexical categorization tasks. They are used for enriching the lexical database of a morphological analyzer with semantic features automatically extracted from the corpora.

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Notes

  1. 1.

    https://code.google.com/p/word2vec/.

References

  1. Banea, C., Chen, D., Mihalcea, R., Cardie, C., Wiebe, J.: SimCompass: using deep learning word embeddings to assess cross-level similarity. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 560–565. Association for Computational Linguistics and Dublin City University, Dublin, Ireland, August 2014

    Google Scholar 

  2. Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 238–247. Association for Computational Linguistics, Baltimore, Maryland, June 2014

    Google Scholar 

  3. Firth, J.R.: A synopsis of linguistic theory 1930–1955. In: Studies in Linguistic Analysis, pp. 1–32 (1957)

    Google Scholar 

  4. Iacobacci, I., Pilehvar, M.T., Navigli, R.: 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, vol. Long Papers, pp. 95–105. Association for Computational Linguistics, Beijing, China, July 2015

    Google Scholar 

  5. Indig, B., Miháltz, M., Simonyi, A.: Exploiting linked linguistic resources for semantic role labeling. In: 7th Language and Technology Conference on Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 140–144, Uniwersytet im. Adama Mickiewicza w Poznaniu, Poznań (2015)

    Google Scholar 

  6. van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-SNE (2008)

    Google Scholar 

  7. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)

    Google Scholar 

  8. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of a 26 and 27th Annual Conference on Neural Information Processing Systems and Advances in Neural Information Processing Systems 2013, 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 3111–3119 (2013)

    Google Scholar 

  9. Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings and Conference of the North American Chapter of the Association of Computational Linguistics: Human Language Technologies, 9–14 June 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA, pp. 746–751 (2013)

    Google Scholar 

  10. Novák, A.: A new form of Humor–mapping constraint-based computational morphologies to a finite-state representation. In: Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 1068–1073. European Language Resources Association (ELRA), Reykjavík, Iceland, aCL Anthology Identifier: L14–1207, May 2014

    Google Scholar 

  11. Novák, A.: Milyen a jó Humor? [What is good Humor like?]. In: I. Magyar Számítógépes Nyelvészeti Konferencia [First Hungarian Conference on Computational Linguistics], pp. 138–144. SZTE, Szeged (2003)

    Google Scholar 

  12. Novák, A.: Improving corpus annotation quality using word embedding models. Polibits 53, pp. 49–53 (2016)

    Google Scholar 

  13. Novák, A., Siklósi, B., Oravecz, C.: A new integrated open-source morphological analyzer for Hungarian. In: Calzolari, N., Choukri, K., Declerck, T., Grobelnik, M., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association (ELRA), Portoroẑ, Slovenia, May 2016

    Google Scholar 

  14. Oravecz, C., Dienes, P.: Efficient stochastic part-of-speech tagging for Hungarian. In: LREC European Language Resources Association (2002)

    Google Scholar 

  15. Orosz, Gy., Novák, A.: PurePos 2.0: a hybrid tool for morphological disambiguation. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2013), pp. 539–545. INCOMA Ltd., Hissar, Bulgaria (2013)

    Google Scholar 

  16. Pereira, F., Tishby, N., Lee, L.: Distributional clustering of English words. In: Proceedings of the 31st Annual Meeting on Association for Computational Linguistics, ACL 1993, pp. 183–190, Association for Computational Linguistics, Stroudsburg, PA, USA (1993)

    Google Scholar 

  17. Prószéky, G., Kis, B.: A unification-based approach to morpho-syntactic parsing of agglutinative and other (highly) inflectional languages. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL 1999, pp. 261–268. Association for Computational Linguistics, Stroudsburg, PA, USA (1999)

    Google Scholar 

  18. Trask, A., Michalak, P., Liu, J.: sense2vec - A fast and accurate method for word sense disambiguation in neural word embeddings. CoRR abs/1511.06388 (2015)

    Google Scholar 

  19. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)

    Article  MathSciNet  Google Scholar 

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Acknowledgment

We thank Márton Bartók for his help in evaluating automatic semantic categorization results.

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Authors and Affiliations

  1. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 50/a Práter street, Budapest, 1083, Hungary

    Borbála Siklósi

Authors
  1. Borbála Siklósi
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Correspondence to Borbála Siklósi .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Siklósi, B. (2018). Using Embedding Models for Lexical Categorization in Morphologically Rich Languages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75476-5

  • Online ISBN: 978-3-319-75477-2

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