Abstract
Named Entity Recognition is a challenging Natural Language Processing task for a language as rich as Portuguese. For this task, a Deep Learning architecture based on bidirectional Long Short-Term Memory with Conditional Random Fields has shown state-of-the-art performance for English, Spanish, Dutch and German languages. In this work, we evaluate this architecture and perform the tuning of hyperparameters for Portuguese corpora. The results achieve state-of-the-art performance using the optimal values for them, improving the results obtained for Portuguese language to up to 5 points in the F1 score.
Thanks to Data-H Data Science and Artificial Intelligence (www.datah.com.br) and Aviso Urgente (https://avisourgente.com.br) for the financial support, and to Cicero Nogueira dos Santos for kindly sharing insights regarding the HAREM corpora.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
This is because I indicates an internal token in the named entity, and O indicates a non-entity token, which means that anything after it would be the starting token of an entity or another non-entity token. Since the first token of a named entity starts with B, according to the IOB scheme, it is not possible that an internal entity token follows a non-entity token.
References
How Much Data is Created on the Internet Each Day? https://blog.microfocus.com/how-much-data-is-created-on-the-internet-each-day/. Accessed 19 Mar 2018
Maynard, D., Bontcheva, K., Augenstein, I.: Natural Language Processing for the Semantic Web, 1st edn. Morgan and Claypool, San Rafael (2017)
dos Santos, C., Guimarães, V.: Boosting named entity recognition with neural character embeddings. arXiv preprint arXiv:1505.05008 (2015)
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. arXiv preprint arxiv:1103.0398 (2011)
Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from Wikipedia. In: Artificial Intelligence, vol. 194, pp. 151–175. Elsevier Science Publishers Ltd., Essex (2013). https://doi.org/10.1016/j.artint.2012.03.006
Chiu, J., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)
Repositório de Word Embeddings do NILC. http://www.nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc. Accessed 30 Mar 2018
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP-2014), vol. 12, pp. 1532–1543 (2014)
Ling, W., Dyer, C., Black, A., Trancoso, I.: Two/too simple adaptations of word2vec for syntax problems. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arxiv:1301.3781 (2013)
Amaral, D., Vieira, R.: NERP-CRF: a tool for the named entity recognition using conditional random fields. In: Linguamática, vol. 6, pp. 41–49 (2014)
Marrero, M., Urbano, J., Sánchez-Cuadrado, S., Morato, J., Gómez-Berbís, J.: Named entity recognition: fallacies, challenges and opportunities. Comput. Stand. Interfaces 35, 482–489 (2013)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157–166 (1994). https://doi.org/10.1109/72.279181
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Sang, E., Veenstra, J.: Representing text chunks. arXiv preprint arxiv:cs/9907006 (1999)
HAREM: Reconhecimento de entidades mencionadas em português. https://www.linguateca.pt/HAREM/. Accessed 21 Mar 2018
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., Aluisio, S.: Portuguese word embeddings: evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Quinta de Castro, P.V., Félix Felipe da Silva, N., da Silva Soares, A. (2018). Portuguese Named Entity Recognition Using LSTM-CRF. In: Villavicencio, A., et al. Computational Processing of the Portuguese Language. PROPOR 2018. Lecture Notes in Computer Science(), vol 11122. Springer, Cham. https://doi.org/10.1007/978-3-319-99722-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-99722-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99721-6
Online ISBN: 978-3-319-99722-3
eBook Packages: Computer ScienceComputer Science (R0)