SenseDependency-Rank: A Word Sense Disambiguation Method Based on Random Walks and Dependency Trees (original) (raw)

Abstract

Word Sense Disambiguation (WSD) is the field that seeks to determine the correct sense of a word in a given context. In this paper, we present a WSD method based on random walks over a dependency tree, whose nodes are word-senses from the WordNet. Besides, our method incorporates prior knowledge about the frequency of use of the word-senses. We observed that our results outperform several graph-based WSD methods in All-Word task of SensEval-2 and SensEval-3, including the baseline, where the nouns and verbs part-of-speech show the better improvement in their F-measure scores.

Similar content being viewed by others

References

  1. Agirre, E., Edmonds, P.: Word Sense Disambiguation: Algorithms and Applications, 1st edn. Springer, Heidelberg (2007). https://doi.org/10.1007/978-1-4020-4809-8
    Book Google Scholar
  2. Agirre, E., López de Lacalle, O., Soroa, A.: Random walks for knowledge-based word sense disambiguation. Comput. Linguist. 40(1), 57–84 (2014)
    Article Google Scholar
  3. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the Seventh International Conference on World Wide Web 7, WWW7, pp. 107–117. Elsevier Science Publishers B. V., Amsterdam (1998)
    Google Scholar
  4. Chaplot, D.S., Bhattacharyya, P., Paranjape, A.: Unsupervised word sense disambiguation using markov random field and dependency parser. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25–30, 2015, Austin, Texas, USA, pp. 2217–2223 (2015)
    Google Scholar
  5. Chen, D., Manning, C.: A Fast and Accurate Dependency Parser using Neural Networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 740–750. Association for Computational Linguistics, Doha, October 2014
    Google Scholar
  6. Edmonds, P., Cotton, S.: Senseval-2: Overview. In: The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems, SENSEVAL 2001, pp. 1–5. Association for Computational Linguistics, Stroudsburg (2001)
    Google Scholar
  7. Fellbaum, C. (ed.): WordNet An Electronic Lexical Database. The MIT Press, Cambridge (1998)
    Google Scholar
  8. Fellbaum, C.: Wordnet and wordnets. In: Brown, K. (ed.) Encyclopedia of Language and Linguistics, pp. 665–670. Elsevier (2005). http://wordnet.princeton.edu/
  9. Gao, N., Zuo, W., Dai, Y., Lv, W.: Word sense disambiguation using wordnet semantic knowledge. In: Wen, Z., Li, T. (eds.) Knowledge Engineering and Management. AISC, vol. 278, pp. 147–156. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54930-4_15
    Chapter Google Scholar
  10. Haveliwala, T.H.: Topic-sensitive pagerank. In: Proceedings of the 11th International Conference on World Wide Web, WWW 2002, pp. 517–526. ACM, New York (2002)
    Google Scholar
  11. Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn. Prentice-Hall Inc., Upper Saddle River (2009)
    Google Scholar
  12. de Marneffe, M.C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: Proceedings of International Conference on Language Resources and Evaluation. LREC, pp. 449–454 (2006)
    Google Scholar
  13. Mihalcea, R.: Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 411–418. Association for Computational Linguistics, Stroudsburg (2005)
    Google Scholar
  14. Navigli, R.: Word sense disambiguation: A survey. ACM Comput. Surv. 41(2), 10:1–10:69 (2009)
    Google Scholar
  15. Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: Similarity: measuring the relatedness of concepts. In: Demonstration Papers at HLT-NAACL 2004, pp. 38–41. Association for Computational Linguistics (2004)
    Google Scholar
  16. Sinha, R.S., Mihalcea, R.: Unsupervised graph-basedword sense disambiguation using measures of word semantic similarity. In: Proceedings of the First IEEE International Conference on Semantic Computing (ICSC 2007), 17–19 September 2007, Irvine, California, USA, pp. 363–369 (2007)
    Google Scholar
  17. Snyder, B., Palmer, M.: The english all-words task. In: Mihalcea, R., Edmonds, P. (eds.) Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 41–43. Association for Computational Linguistics, Barcelona (2004)
    Google Scholar

Download references

Acknowledgments

For this study, the authors acknowledge the support of the “Programa Nacional de Innovación para la Competitividad y Productividad”, Perú, under the contract 124-PNICP-PIAP-2015.

Author information

Authors and Affiliations

  1. Department of Engineering, Research Group on Pattern Recognition and Applied Artificial Intelligence, Pontificia Universidad Católica del Perú, Lima, Peru
    Marco Antonio Sobrevilla-Cabezudo, Arturo Oncevay-Marcos & Andrés Melgar

Authors

  1. Marco Antonio Sobrevilla-Cabezudo
  2. Arturo Oncevay-Marcos
  3. Andrés Melgar

Corresponding author

Correspondence toMarco Antonio Sobrevilla-Cabezudo .

Editor information

Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico
    Alexander Gelbukh

Rights and permissions

© 2018 Springer Nature Switzerland AG

About this paper

Cite this paper

Sobrevilla-Cabezudo, M.A., Oncevay-Marcos, A., Melgar, A. (2018). SenseDependency-Rank: A Word Sense Disambiguation Method Based on Random Walks and Dependency Trees. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7\_15

Download citation

Keywords

Publish with us