Skip to main content
SpringerLink
Log in
Book cover

Conference on Artificial Intelligence and Natural Language

AINL 2018: Artificial Intelligence and Natural Language pp 11–22Cite as

Smart Context Generation for Disambiguation to Wikipedia

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 930))

Abstract

Wikification is a crucial NLP task that aims to identify entities in text and disambiguate their meaning. Being partially solved for English, the problem still remains fairly untouched for Russian. In this article we present a novel approach to Disambiguation to Wikipedia applied to the Russian language. Inspired by the Neural Machine Translation task our method implements encoder-decoder neural network architecture. It translates text tokens into concept embeddings that are subsequently used as context for disambiguation. In order to test our hypothesis we add our context features to GLOW system considered a baseline. Moreover, we present commonly available dataset for the Disambiguation to Wikipedia task.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://github.com/ispras-texterra/ainl-2018-d2w-dataset.

  2. 2.

    Note, that token embedding size is \(101 = 100 + \) extra position to encode END_TOKEN. Similar idea is for concept embedding size and START_CONCEPT/END_CONCEPT.

References

  1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of 3rd International Conference for Learning Representations, San Diego, pp. 1–15 (2015)

    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)

    Google Scholar 

  3. Cheng, X., Roth, D.: Relational inference for wikification. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. pp. 1787–1796 (2013)

    Google Scholar 

  4. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8) (2014)

    Google Scholar 

  5. Dandala, B., Mihalcea, R., Bunescu, R.: Word sense disambiguation using wikipedia. In: Gurevych, I., Kim, J. (eds.) The People’s Web Meets NLP, pp. 241–262. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35085-6_9

    Chapter  Google Scholar 

  6. Durrett, G., Klein, D.: A joint model for entity analysis: coreference, typing, and linking. Trans. Assoc. Comput. Linguist. 2, 477–490 (2014)

    Google Scholar 

  7. Francis-Landau, M., Durrett, G., Klein, D.: Capturing semantic similarity for entity linking with convolutional neural networks. In: Proceedings of NAACL-HLT, pp. 1256–1261 (2016)

    Google Scholar 

  8. Ganea, O.E., Hofmann, T.: Deep joint entity disambiguation with local neural attention (EMNLP 2017). In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2619–2629. Association for Computational Linguistics (2017)

    Google Scholar 

  9. Gehring, J., Auli, M., Grangier, D., Dauphin, Y.: A convolutional encoder model for neural machine translation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 123–135 (2017)

    Google Scholar 

  10. Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: International Conference on Machine Learning, pp. 1243–1252 (2017)

    Google Scholar 

  11. Guo, Z., Barbosa, D.: Robust named entity disambiguation with random walks. Semant. Web 1–21 (2016, preprint)

    Google Scholar 

  12. Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2002)

    Google Scholar 

  13. Li, J., Cai, Y., Cai, Z., Leung, H., Yang, K.: Wikipedia based short text classification method. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10179, pp. 275–286. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55705-2_22

    Chapter  Google Scholar 

  14. Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421 (2015)

    Google Scholar 

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

    Google Scholar 

  16. Milne, D., Witten, I.H.: Learning to link with Wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 509–518. ACM (2008)

    Google Scholar 

  17. Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from Wikipedia. Artif. Intell. 194, 151–175 (2013)

    Article  MathSciNet  Google Scholar 

  18. Ratinov, L., Roth, D., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to Wikipedia. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1375–1384. Association for Computational Linguistics (2011)

    Google Scholar 

  19. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

    Google Scholar 

  20. Sysoev, A., Andrianov, I.: Named entity recognition in Russian: the power of wiki-based approach. In: Proceedings of International Conference “Dialogue-2016”, pp. 746–755 (2016)

    Google Scholar 

  21. Turdakov, D., et al.: Semantic analysis of texts using texterra system (2014). http://www.dialog-21.ru/digests/dialog2014/materials/pdf/TurdakovDY.pdf. Accessed 28 May 2018

  22. Wu, G., He, Y., Hu, X.: Entity linking: an issue to extract corresponding entity with knowledge base. IEEE Access 6, 6220–6231 (2018)

    Article  Google Scholar 

  23. Yamada, I., Ito, T., Takeda, H., Takefuji, Y.: Linkify: enhancing text reading experience by detecting and linking helpful entities to users. IEEE Intell. Syst. (2018)

    Google Scholar 

  24. Zhou, J., Cao, Y., Wang, X., Li, P., Xu, W.: Deep recurrent models with fast-forward connections for neural machine translation. Trans. Assoc. Comput. Linguist. 4(1), 371–383 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ivannikov Institute for System Programming, Russian Academy of Sciences, Moscow, Russia

    Andrey Sysoev & Irina Nikishina

  2. Higher School of Economics, Moscow, Russia

    Irina Nikishina

Authors
  1. Andrey Sysoev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Irina Nikishina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrey Sysoev .

Editor information

Editors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Baden-Württemberg, Germany

    Dmitry Ustalov

  2. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  3. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

  4. Mendel University, Brno, Czech Republic

    Jan Žižka

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sysoev, A., Nikishina, I. (2018). Smart Context Generation for Disambiguation to Wikipedia. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01204-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01203-8

  • Online ISBN: 978-3-030-01204-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation