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Automatic Generation of Annotated Collection for Recognition of Sentiment Frames

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Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12602))

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Abstract

While addressing the challenge of sentiment analysis, it is crucial to consider not only the polarity of certain words but also the polarity between them, particularly between the arguments of a predicate. For this purpose, the RuSentiFrames lexicon was created. But the training of the ML model requires an annotated collection of data, and since the manual annotation is laborious and expensive, the automation of the process is preferable. In this paper, we describe a rule-based approach to automatic annotation of semantic roles for the predicates of the RuSentiFrames lexicon. The implementation of the algorithm includes the search of the entities with certain morpho-syntactic features in the order that depends on the case of the entity and is based on calculation of the posterior probabilities of the co-occurrence of a certain case and a certain type of predicate arguments. The results of the algorithm evaluation, based on several different characteristics, were relatively high. The solutions of problematic cases have been suggested and are expected to be implemented in further research.

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References

  1. Burtsev, M., et al.: DeepPavlov: open-source library for dialogue system. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, pp. 1–6 (2018)

    Google Scholar 

  2. Carreras, X., Marquez, L.: Introduction to the CoNLL-2005 shared task: semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), pp. 152–164. Ann Arbor, MI (2005)

    Google Scholar 

  3. Chomsky, N.: Lectures on Government and Binding. Foris Publications, Dordrecht (1981)

    Google Scholar 

  4. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    MATH  Google Scholar 

  5. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  6. Fillmore, C.: Toward a modern theory of case. In: The Ohio State University Project on Linguistic Analysis, report 13, pp. 1–24. Ohio State University, Columbus (1966)

    Google Scholar 

  7. Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Comput. Linguist. 28(3), 245–288 (2002)

    Article  Google Scholar 

  8. Guan, C., Cheng, Y., Zhao, H.: Semantic role labeling with associated memory network. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 3361–3371. Association for Computational Linguistics, Minneapolis (2019)

    Google Scholar 

  9. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the European Conference on Machine Learning, pp. 137–142 (1998)

    Google Scholar 

  10. Kudo, T., Matsumoto, Y.: Use of support vector learning for chunk identification. In: Proceedings of the 4th Conference on CoNLL-2000 and LLL-2000, pp. 142–144 (2000)

    Google Scholar 

  11. Larionov, D., Shelmanov, A., Chistova, E., Smirnov, I.: Semantic role labeling with pretrained language models for known and unknown predicates. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, pp. 619–628 (2019)

    Google Scholar 

  12. Loukachevitch, N.V., Rusnachenko, N.L.: Sentiment frames for attitude extraction in Russian. In: Proceedings of International Conference Dialog, pp. 541–552 (2020)

    Google Scholar 

  13. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. J. Mach. Learn. Res. 2(Feb), 419–444 (2002)

    MATH  Google Scholar 

  14. Lyashevskaya O.: Dictionary of valencies meets corpus annotation: a case of Russian framebank. In: Proceedings of the 15th EURALEX International Congress, Oslo, Norway, 7–11 August 2012, p. 15 (2012)

    Google Scholar 

  15. Lyashevskaya O., Kashkin E.: FrameBank: a database of Russian lexical constructions. In: Proceedings of International Conference on Analysis of Images, Social Networks and Texts, pp. 350–360 (2015)

    Google Scholar 

  16. Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31(1), 71–106 (2005)

    Article  Google Scholar 

  17. Pradhan, S., Hacioglu, K., Krugler, V., Ward, W., Martin, J.H., Jurafsky, D.: Support vector learning for semantic argument classification. Mach. Learn. 60, 11–39 (2005)

    Article  Google Scholar 

  18. Rusachenko, N., Loukachevitch, N., Tutubalina, E.: Distant Supervision for Sentiment Attitude Extraction. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 1022–1030 (2019)

    Google Scholar 

  19. Shelmanov, A.O., Devyatkin, D.A.: Semantic role labeling with neural networks for texts in Russian. In: Proceedings of International Conference Dialogue, pp. 245–256 (2017)

    Google Scholar 

  20. Shelmanov, A.O., Smirnov, I.V.: Methods for semantic role labeling of Russian texts. In: Proceedings of International Conference Dialog, pp. 607–620 (2014)

    Google Scholar 

  21. Ustalov, D., Panchenko, A., Kutuzov, A., Biemann, C., Ponzetto, S.P.: Unsupervised semantic frame induction using triclustering. arXiv preprint arXiv:1805.04715 (2018)

  22. Zhou, J., Xu, W. End-to-end learning of semantic role labeling using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1127–1137 (2015)

    Google Scholar 

  23. Apresyan, Y.D. Leksicheskaya semantika. Nauka, Moscow (1974)

    Google Scholar 

  24. Apresyan, Y.D., Boguslavskij, I.M., Iomdin, L.L., et al.: Lingvisticheskoe obespechenie sistemy ETAP-2. Nauka, Moscow (1989)

    Google Scholar 

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Acknowledgements

The reported study was funded by RFBR according to the research project N 20-07-01059.

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Solomatina, Y., Loukachevitch, N. (2021). Automatic Generation of Annotated Collection for Recognition of Sentiment Frames. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-72610-2_12

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  • Print ISBN: 978-3-030-72609-6

  • Online ISBN: 978-3-030-72610-2

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