Skip to main content
Book cover

International Conference on Artificial Intelligence and Soft Computing

ICAISC 2019: Artificial Intelligence and Soft Computing pp 39–50Cite as

Combining Neural and Knowledge-Based Approaches to Named Entity Recognition in Polish

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11508)

Abstract

Named entity recognition (NER) is one of the tasks in natural language processing that can greatly benefit from the use of external knowledge sources. We propose a named entity recognition framework composed of knowledge-based feature extractors and a deep learning model including contextual word embeddings, long short-term memory (LSTM) layers and conditional random fields (CRF) inference layer. We use an entity linking module to integrate our system with Wikipedia. The combination of effective neural architecture and external resources allows us to obtain state-of-the-art results on recognition of Polish proper names. We evaluate our model on the data from PolEval 2018 (http://2018.poleval.pl/) NER challenge on which it outperforms other methods, reducing the error rate by 22.4% compared to the winning solution.

Keywords

  • Named entity recognition
  • Wikipedia
  • Entity linking

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-20912-4_4
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-20912-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://sjp.pl.

  2. 2.

    https://github.com/sdadas/wiki-mapper.

  3. 3.

    https://www.wikipedia.org, https://babelnet.org, https://wiki.dbpedia.org.

  4. 4.

    https://en.wikipedia.org/wiki/SemEval.

  5. 5.

    https://github.com/morfologik/morfologik-stemming.

References

  1. Akbik, A., Blythe, D., Vollgraf, R.: Contextual string embeddings for sequence labeling. In: 27th International Conference on Computational Linguistics, COLING 2018, pp. 1638–1649 (2018)

    Google Scholar 

  2. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    CrossRef  Google Scholar 

  3. Borchmann, Ł., Gretkowski, A., Graliński, F.: Approaching nested named entity recognition with parallel LSTM-CRFs. In: Proceedings of AI and NLP Workshop Day 2018 (2018)

    Google Scholar 

  4. Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)

    CrossRef  Google Scholar 

  5. Cilibrasi, R.L., Vitanyi, P.M.: The Google similarity distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)

    CrossRef  Google Scholar 

  6. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)

    Google Scholar 

  7. Dozat, T.: Incorporating Nesterov momentum into ADAM. In: ICLR (2016)

    Google Scholar 

  8. Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019–1027 (2016)

    Google Scholar 

  9. Graliński, F., Jassem, K., Marcińczuk, M., Wawrzyniak, P.: Named entity recognition in machine anonymization. In: Recent Advances in Intelligent Information Systems (2009)

    Google Scholar 

  10. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. CoRR abs/1508.01991 (2015)

    Google Scholar 

  11. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT, pp. 260–270 (2016)

    Google Scholar 

  12. Liu, L., et al.: Empower sequence labeling with task-aware neural language model. arXiv preprint arXiv:1709.04109 (2017)

  13. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1064–1074 (2016)

    Google Scholar 

  14. Marcińczuk, M., Kocoń, J., Gawor, M.: Recognition of named entities for Polish-comparison of deep learning and conditional random fields approaches. In: Ogrodniczuk, M., Kobyliński, Ł. (eds.) Proceedings of the PolEval 2018 Workshop, pp. 77–92. Institute of Computer Science, Polish Academy of Science (2018)

    Google Scholar 

  15. Marcińczuk, M., Kocoń, J., Janicki, M.: Liner2-a customizable framework for proper names recognition for Polish. In: Bembenik, R., Skonieczny, L., Rybinski, H., Kryszkiewicz, M., Niezgodka, M. (eds.) Intelligent Tools for Building a Scientific Information Platform. SCI, vol. 467, pp. 231–253. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35647-6_17

    CrossRef  Google Scholar 

  16. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  17. Miłkowski, M.: Developing an open-source, rule-based proofreading tool. Softw. Pract. Exp. 40(7), 543–566 (2010)

    Google Scholar 

  18. Milne, D., Witten, I.H.: An open-source toolkit for mining Wikipedia. Artif. Intell. 194, 222–239 (2013)

    CrossRef  MathSciNet  Google Scholar 

  19. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  20. Peters, M., Ammar, W., Bhagavatula, C., Power, R.: Semi-supervised sequence tagging with bidirectional language models. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1756–1765 (2017)

    Google Scholar 

  21. Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 2227–2237 (2018)

    Google Scholar 

  22. Piskorski, J.: Named-entity recognition for Polish with SProUT. In: Bolc, L., Michalewicz, Z., Nishida, T. (eds.) IMTCI 2004. LNCS (LNAI), vol. 3490, pp. 122–133. Springer, Heidelberg (2005). https://doi.org/10.1007/11558637_13

    CrossRef  Google Scholar 

  23. Piskorski, J., Schäfer, U., Xu, F.: Shallow processing with unification and typed feature structures-foundations and applications. Knstliche Intelligenz 1(1), 17–23 (2004)

    Google Scholar 

  24. Pohl, A.: Knowledge-based named entity recognition in Polish. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 145–151. IEEE (2013)

    Google Scholar 

  25. Przepiórkowski, A., Banko, M., Górski, R.L., Lewandowska-Tomaszczyk, B.: National Corpus of Polish. Polish Scientific Publishers PWN, Warsaw (2012)

    Google Scholar 

  26. Radziszewski, A.: A tiered CRF tagger for Polish. Intelligent Tools for Building a Scientific Information Platform, vol. 467, pp. 215–230. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35647-6_16

    CrossRef  Google Scholar 

  27. Rei, M.: Semi-supervised multitask learning for sequence labeling. arXiv preprint arXiv:1704.07156 (2017)

  28. Reimers, N., Gurevych, I.: Optimal hyperparameters for deep LSTM-networks for sequence labeling tasks. arXiv preprint arXiv:1707.06799 (2017)

  29. dos Santos, C., Guimaraes, V., Niterói, R., de Janeiro, R.: Boosting named entity recognition with neural character embeddings. In: Proceedings of NEWS 2015 the Fifth Named Entities Workshop, p. 25 (2015)

    Google Scholar 

  30. Waszczuk, J.: NERF - named entity recognition tool based on linear-chain CRFs (2012). http://zil.ipipan.waw.pl/Nerf

  31. Wolinski, M., Milkowski, M., Ogrodniczuk, M., Przepiórkowski, A.: PoliMorf: a (not so) new open morphological dictionary for Polish. In: LREC, pp. 860–864 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Information Processing Institute, Warsaw, Poland

    Sławomir Dadas

Authors
  1. Sławomir Dadas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sławomir Dadas .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Prof. Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Dr. Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Dr. Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Prof. Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Dr. Jacek M. Zurada

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Dadas, S. (2019). Combining Neural and Knowledge-Based Approaches to Named Entity Recognition in Polish. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20912-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

  • eBook Packages: Computer ScienceComputer Science (R0)