Skip to main content

Learning Word Embeddings from Portuguese Lexical-Semantic Knowledge Bases

  • Conference paper
  • First Online:
Computational Processing of the Portuguese Language (PROPOR 2018)

Abstract

This paper describes the creation of PT-LKB, new Portuguese word embeddings learned from a large lexical-semantic knowledge base (LKB), using the node2vec method. Resulting embeddings combine the strengths of word vector representations and, even with lower dimensions, achieve high scores in genuine similarity, which so far were obtained by exploiting the graph structure of LKBs.

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

Access this chapter

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

Institutional subscriptions

Notes

  1. 1.

    http://ontopt.dei.uc.pt/index.php?sec=download_outros.

  2. 2.

    http://snap.stanford.edu/node2vec/.

References

  1. Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)

    Article  Google Scholar 

  2. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  3. Gonçalo Oliveira, H.: CONTO.PT: groundwork for the automatic creation of a fuzzy Portuguese WordNet. In: Silva, J., Ribeiro, R., Quaresma, P., Adami, A., Branco, A. (eds.) PROPOR 2016. LNCS, vol. 9727, pp. 283–295. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41552-9_29

    Chapter  Google Scholar 

  4. Gonçalo Oliveira, H.: Distributional and knowledge-based approaches for computing Portuguese word similarity. Information 9(2), 35 (2018)

    Article  Google Scholar 

  5. Granada, R., Trojahn, C., Vieira, R.: Comparing semantic relatedness between word pairs in Portuguese using Wikipedia. In: Baptista, J., Mamede, N., Candeias, S., Paraboni, I., Pardo, T.A.S., Volpe Nunes, M.G. (eds.) PROPOR 2014. LNCS, vol. 8775, pp. 170–175. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09761-9_17

    Chapter  Google Scholar 

  6. Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 855–864. ACM (2016)

    Google Scholar 

  7. Hartmann, N.S., Fonseca, E.R., Shulby, C.D., Treviso, M.V., Rodrigues, J.S., AluĂ­sio, S.M.: Portuguese word embeddings: evaluating on word analogies and natural language tasks. In: Proceedings the 11th Brazilian Symposium in Information and Human Language Technology, STIL 2017 (2017)

    Google Scholar 

  8. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the Workshop Track of the International Conference on Learning Representations, ICLR, Scottsdale, Arizona (2013)

    Google Scholar 

  9. Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet::Similarity: measuring the relatedness of concepts. In: Demonstration Papers at HLT-NAACL 2004, pp. 38–41. ACL Press, Stroudsburg (2004)

    Google Scholar 

  10. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing, EMNLP, pp. 1532–1543 (2014)

    Google Scholar 

  11. Pilehvar, M.T., Jurgens, D., Navigli, R.: Align, disambiguate and walk: a unified approach for measuring semantic similarity. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, Sofia, Bulgaria, Long Papers, vol. 1, pp. 1341–1351. ACL Press (2013)

    Google Scholar 

  12. Querido, A., et al.: LX-LR4DistSemEval: a collection of language resources for the evaluation of distributional semantic models of Portuguese. Revista da AssociaĂ§Ă¡o Portuguesa de LinguĂ­stica 3, 265–283 (2017)

    Google Scholar 

  13. Rodrigues, J., Branco, A., Neale, S., Silva, J.: LX-DSemVectors: distributional semantics models for Portuguese. In: Silva, J., Ribeiro, R., Quaresma, P., Adami, A., Branco, A. (eds.) PROPOR 2016. LNCS, vol. 9727, pp. 259–270. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41552-9_27

    Chapter  Google Scholar 

  14. Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of 31st AAAI Conference on Artificial Intelligence, San Francisco, California, USA, pp. 4444–4451 (2017)

    Google Scholar 

  15. Vylomova, E., Rimell, L., Cohn, T., Baldwin, T.: Take and took, gaggle and goose, book and read: evaluating the utility of vector differences for lexical relation learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1671–1682. ACL Press (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hugo Gonçalo Oliveira .

Editor information

Editors and Affiliations

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

Gonçalo Oliveira, H. (2018). Learning Word Embeddings from Portuguese Lexical-Semantic Knowledge Bases. 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_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99722-3_27

  • 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)

Publish with us

Policies and ethics