Abstract
This work presents a literal and metaphorical language classifier for the Trofi corpus (Gao G. et al. 2018), through LSTM cells, comparing the results for the use of three pretrained language models RoBERTa-large, RoBERTa-base and BERT-base uncased. Through this article, it is proposed to address three fundamental points of the study of metaphorical language: the different tools for its vectorial representation, the use of LSTM cells to work metaphorical language and the impotence of the central task presented, its classification. Finally the results are compared against the state of the art and that work presents some observations as a conclusion.
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References
Lakoff, G., Johnson, M.: Metaphors We Live By. University of Chicago press, Chicago (2008)
Shutova, E., Teufel, S., Korhonen, A.: Statistical metaphor processing. Comput. Linguist. 39(2), 301–353 (2013)
Mohammad, S., Shutova, E., Turney, P.: Metaphor as a medium for emotion: an empirical study. In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, pp. 23–33 (2016)
Le, D., Thai, M., Nguyen, T.: Multi-task learning for metaphor detection with graph convolutional neural networks and word sense disambiguation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(05), pp. 8139–8146) (2020)
Wilks, Y., Dalton, A., Allen, J., Galescu, L.: Automatic metaphor detection using large-scale lexical resources and conventional metaphor extraction. In Proceedings of the First Workshop on Metaphor in NLP, pp. 36–44 (2013)
Maudslay, R.H., Pimentel, T., Cotterell, R., Teufel, S.: Metaphor detection using context and concreteness. In Proceedings of the Second Workshop on Figurative Language Processing, pp. 221–226. Association for Computational Linguistics (2020)
Ruder, S., Vulić, I., Søgaard, A.: A survey of cross-lingual word embedding models. J. Artif. Intell. Res. 65, 569–631 (2019)
Mao, R., Lin, C., Guerin, F.: End-to-end sequential metaphor identification inspired by linguistic theories. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3888–3898 (2019)
Rohanian, O., Rei, M., Taslimipoor, S., Ha, L.: Verbal multiword expressions for identification of metaphor. ACL (2020)
Pilehvar, M.T., Camacho-Collados, J.: Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. Morgan and Claypool Publishers, San Rafael (2020)
Gao, G., Choi, E., Choi, Y., Zettlemoyer, L.: Neural metaphor detection in context. arXiv preprint arXiv:1808.09653 (2018)
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Ovando-Becerril, E., Calvo, H. (2024). A Metaphorical Text Classifier to Compare the Use of RoBERTa-Large, RoBERTa-Base and BERT-Base Uncased. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_22
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DOI: https://doi.org/10.1007/978-3-031-49552-6_22
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