Advertisement

Portuguese Named Entity Recognition Using LSTM-CRF

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11122)

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.

Keywords

Natural Language Processing Named Entity Recognition Deep learning Neural networks Portuguese language 

References

  1. 1.
    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
  2. 2.
    Maynard, D., Bontcheva, K., Augenstein, I.: Natural Language Processing for the Semantic Web, 1st edn. Morgan and Claypool, San Rafael (2017)Google Scholar
  3. 3.
    dos Santos, C., Guimarães, V.: Boosting named entity recognition with neural character embeddings. arXiv preprint arXiv:1505.05008 (2015)
  4. 4.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)
  5. 5.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. arXiv preprint arxiv:1103.0398 (2011)
  6. 6.
    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.2012.03.006MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chiu, J., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)
  8. 8.
    Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)
  9. 9.
    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
  10. 10.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
  11. 11.
    Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arxiv:1301.3781 (2013)
  15. 15.
    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)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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/10.1109/72.279181CrossRefGoogle Scholar
  18. 18.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997).  https://doi.org/10.1162/neco.1997.9.8.1735CrossRefGoogle Scholar
  19. 19.
    Sang, E., Veenstra, J.: Representing text chunks. arXiv preprint arxiv:cs/9907006 (1999)
  20. 20.
    HAREM: Reconhecimento de entidades mencionadas em português. https://www.linguateca.pt/HAREM/. Accessed 21 Mar 2018
  21. 21.
    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)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Universidade Federal de GoiásGoiâniaBrazil

Personalised recommendations