Universal Dependencies for Tweets in Brazilian Portuguese: Tokenization and Part of Speech Tagging
Resumo
Automatically dealing with Natural Language User-Generated Content (UGC) is a challenging task of utmost importance, given the amount of information available over the web. We present in this paper an effort on building tokenization and Part of Speech (PoS) tagging systems for tweets in Brazilian Portuguese, following the guidelines of the Universal Dependencies (UD) project. We propose a rule-based tokenizer and the customization of current state-of-the-art UD-based tagging strategies for Portuguese, achieving a 98% f-score for tokenization, and a 95% f-score for PoS tagging. We also introduce DANTEStocks, the corpus of stock market tweets on which we base our work, presenting preliminary evidence of the multi-genre capacity of our PoS tagger.
Referências
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Fonseca, E., Rosa, J., and Aluísio, S. (2015). Evaluating word embeddings and a revised corpus for part-of-speech tagging in portuguese. Journal of the Brazilian Computer Society, 21(2):1–14.
Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrialstrength Natural Language Processing in Python.
Hovy, E. and Lavid, J. (2010). Towards a ‘science’ of corpus annotation: A new methodological challenge for corpus linguistics. International Journal of Translation, 22(1):13–36.
Kondratyuk, D. and Straka, M. (2019). 75 languages, 1 model: Parsing Universal Dependencies universally. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2779–2795, Hong Kong, China. Association for Computational Linguistics.
Liu, Y., Zhu, Y., Che, W., Qin, B., Schneider, N., and Smith, N. A. (2018). Parsing tweets into Universal Dependencies. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 965–975, New Orleans, Louisiana. Association for Computational Linguistics.
Loper, E. and Bird, S. (2002). Nltk: The natural language toolkit. In In Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Philadelphia: Association for Computational Linguistics.
Marcus, M. P., Santorini, B., and Marcinkiewicz, M. A. (1993). Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313–330.
Nivre, J., de Marneffe, M.-C., Ginter, F., Goldberg, Y., Hajic, J., Manning, C. D., Mc-Donald, R., Petrov, S., Pyysalo, S., Silveira, N., Tsarfaty, R., and Zeman, D. (2016). Universal Dependencies v1: A multilingual treebank collection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 1659–1666.
Nivre, J., de Marneffe, M.-C., Ginter, F., Hajic, J., Manning, C. D., Pyysalo, S., Schuster, S., Tyers, F., and Zeman, D. (2020). Universal Dependencies v2: An evergrowing multilingual treebank collection. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4034–4043.
Rademaker, A., Chalub, F., Real, L., Freitas, C., Bick, E., and de Paiva, V. (2017). Universal dependencies for portuguese. In Proceedings of the Fourth International Conference on Dependency Linguistics (Depling), pages 197–206, Pisa, Italy.
Sanguinetti, M., Bosco, C., Cassidy, L., Çetinoglu, Ö ., Cignarella, A. T., Lynn, T., Rehbein, I., Ruppenhofer, J., Seddah, D., and Zeldes, A. (2020). Treebanking usergenerated content: A proposal for a unified representation in Universal Dependencies. In Proceedings of the 12th Language Resources and Evaluation Conference.
Sanguinetti, M., Bosco, C., Lavelli, A., Mazzei, A., Antonelli, O., and Tamburini, F. (2018). PoSTWITA-UD: an Italian Twitter treebank in Universal Dependencies. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
Straka, M. (2018). UDPipe 2.0 prototype at CoNLL 2018 UD shared task. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 197–207, Brussels, Belgium. Association for Computational Linguistics.
Straka, M., Hajic, J., and Straková, J. (2016). UDPipe: Trainable pipeline for processing CoNLL-U files performing tokenization, morphological analysis, POS tagging and parsing. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 4290–4297, Portoroz, Slovenia. European Language Resources Association (ELRA).
Vieira da Silva, F. J., Roman, N. T., and Carvalho, A. M. (2020). Stock market tweets annotated with emotions. Corpora, 15(3):343–354.