Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-13T06:31:39.504Z Has data issue: false hasContentIssue false
  • English
  • Français

CO-graph: A new graph-based technique for cross-lingual word sense disambiguation

Published online by Cambridge University Press:  16 April 2015

ANDRES DUQUE ,
LOURDES ARAUJO  and
JUAN MARTINEZ-ROMO
ANDRES DUQUE
Affiliation:
Dpto. Lenguajes y Sistemas Informáticos, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain e-mail: aduque@lsi.uned.es, lurdes@lsi.uned.es, juaner@lsi.uned.es
LOURDES ARAUJO
Affiliation:
Dpto. Lenguajes y Sistemas Informáticos, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain e-mail: aduque@lsi.uned.es, lurdes@lsi.uned.es, juaner@lsi.uned.es
JUAN MARTINEZ-ROMO
Affiliation:
Dpto. Lenguajes y Sistemas Informáticos, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain e-mail: aduque@lsi.uned.es, lurdes@lsi.uned.es, juaner@lsi.uned.es
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most suitable translations from the dictionary. Different algorithms that combine both the dictionary and the co-occurrence graph are then used for performing this selection of the final translations: techniques based on sub-graphs (communities) containing clusters of words with related meanings, based on distances between nodes representing words, and based on the relative importance of each node in the whole graph. The initial output of the system is enhanced with translation probabilities, provided by a statistical bilingual dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 2010, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed and compared to those obtained by the systems participating in the competitions. Our system offers the best results in comparison with other unsupervised systems in most of the experiments, and even overcomes supervised systems in some cases.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Special Issue 5: Graphs in NLP , November 2015 , pp. 743 - 772
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agirre, E., and Soroa, A. 2009. Personalizing pagerank for word sense disambiguation. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL-2009), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 33–41.Google Scholar
Agirre, E., Lopez de Lacalle, O., and Soroa, A., 2014. Random walks for knowledge-based word sense disambiguation. Computational Linguistics 40 (1): 57–84.CrossRefGoogle Scholar
Apidianaki, M. 2008. Translation-oriented word sense induction based on parallel corpora. In Proceedings of the 6th International Language Resources and Evaluation (LREC-08), Marrakech, Morocco, May. European Language Resources Association (ELRA).Google Scholar
Apidianaki, M., 2009. Data-driven semantic analysis for multilingual wsd and lexical selection in translation. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL-2009), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 77–85.CrossRefGoogle Scholar
Apidianaki, M. 2013. Limsi: cross-lingual word sense disambiguation using translation sense clustering. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval-2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics.Google Scholar
Banea, C., and Mihalcea, R. 2011. Word sense disambiguation with multilingual features. In Proceedings of the Ninth International Conference on Computational Semantics (IWCS -2011), Association for Computational Linguistics, pp. 25–34.Google Scholar
Biemann, C., 2006. Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In Proceedings of the 1st Workshop on Graph Based Methods for Natural Language Processing, TextGraphs-1, Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 73–80.CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3: 993–1022, March.Google Scholar
Brin, S., and Page, L. 1998. The anatomy of a large-scale hypertextual web search engine. In Computer Networks and ISDN Systems, Elsevier Science Publishers B. V., pp. 107–117.Google Scholar
Carpuat, M., 2013. Nrc: a machine translation approach to cross-lingual word sense disambiguation (semeval-2013 task 10). In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval-2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics, pp. 188–192.Google Scholar
Chan, Y. S., Ng, H. T., and Chiang, D. 2007. Word sense disambiguation improves statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), pp. 33–40.Google Scholar
Dandala, B., Mihalcea, R., and Bunescu, R. 2013. Multilingual word sense disambiguation using wikipedia. In Proceedings of the 6th International Joint Conference on Natural Language Processing. Asian Federation of Natural Language Processing.CrossRefGoogle Scholar
Diab, M. T. and Resnik, P. 2002. An unsupervised method for word sense tagging using parallel corpora. In ACL, pp. 255–262.Google Scholar
Dijkstra, E. W., 1959. A note on two problems in connexion with graphs. Numerische Mathematik 1 (1): 269–271.CrossRefGoogle Scholar
Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. Bradford Books.CrossRefGoogle Scholar
Fernandez-Ordonez, E., Mihalcea, R., and Hassan, S. 2012. Unsupervised word sense disambiguation with multilingual representations. In LREC, pp. 847–851.Google Scholar
Guo, W., and Diab, M., 2010. Coleur and colslm: a wsd approach to multilingual lexical substitution, tasks 2 and 3 semeval 2010. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval-2010), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 129–133.Google Scholar
Ide, N. and Veronis, J., 1998. Word sense disambiguation: the state of the art. Computational Linguistics 24 : 1–40.Google Scholar
Ion, R., and Tufis, D., 2004. Multilingual word sense disambiguation using aligned wordnets. Romanian Journal of Information Science and Technology 7 (1–2): 183–200.Google Scholar
Kazakov, D., and Shahid, A. R. 2010. Retrieving lexical semantics from multilingual corpora. In Polibits, pp. 25–28.Google Scholar
Kazakov, D., and Shahid, A. R. 2013. Using parallel corpora for word sense disambiguation. In Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP-2013), Shoumen, Bulgaria, INCOMA Ltd.Google Scholar
Koehn, P. 2005. Europarl: a parallel corpus for statistical machine translation. In MT summit, volume 5.Google Scholar
Lefever, E., and Hoste, V., 2010a. Semeval-2010 task 3: cross-lingual word sense disambiguation. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval-2010), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 15–20.CrossRefGoogle Scholar
Lefever, E., and Hoste, V. 2010b. Construction of a benchmark data set for cross-lingual word sense disambiguation. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC-2010), Valletta, Malta, May. European Language Resources Association (ELRA).Google Scholar
Lefever, E., and Hoste, V., 2013. Semeval-2013 task 10: cross-lingual word sense disambiguation. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval-2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics, pp. 158–166.Google Scholar
Lefever, E., Hoste, V., and De Cock, M., 2011. Parasense or how to use parallel corpora for word sense disambiguation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2 (HLT2011), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 317–322.Google Scholar
Màrquez, L., Exsudero, G., Martínez, D., and Rigau, G. 2006. Supervised corpus-based methods for wsd. In Word Sense Disambiguation: Algorithms and Applications, vol. 33, pp. 167–216. Text, Speech and Language Technology. Dordrecht, The Netherlands: Springer.Google Scholar
Martinez-Romo, J., Araujo, L., Borge-Holthoefer, J., Arenas, A., Capitán, J. A., and Cuesta, J. A. 2011. Disentangling categorical relationships through a graph of co-occurrences. Physical Review E 84: 046108, October.CrossRefGoogle ScholarPubMed
Mihalcea, R., 2005. Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data iza ling. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT-2005), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 411–418.CrossRefGoogle Scholar
Mihalcea, R. 2006. Knowledge-based methods for wsd. In Word Sense Disambiguation: Algorithms and Applications, vol. 33, pp. 107–132. Text, Speech and Language Technology. Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
Navigli, R., and Lapata, M. 2010. An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (4): 678–692, April.CrossRefGoogle ScholarPubMed
Navigli, R., and Ponzetto, S. P. 2010. Babelnet: building a very large multilingual semantic network. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-2010), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 216–225.Google Scholar
Navigli, R., and Ponzetto, S. P., 2012. Joining forces pays off: multilingual joint word sense disambiguation. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL-2012), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 1399–1410.Google Scholar
Och, F. J., and Ney, H. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics 29 (1): 19–51, March.CrossRefGoogle Scholar
Pons, P., and Latapy, M., 2005. Computing communities in large networks using random walks. Lecture Notes in Computer Science 3733 : 284.CrossRefGoogle Scholar
Reese, S., Boleda, G., Cuadros, M., Padr, L., and Rigau, G. 2010. Wikicorpus: a word-sense disambiguated multilingual wikipedia corpus. In N. Calzolari, K.Choukri, B.Maegaard, J.Mariani, J.Odijk, S.Piperidis, M. Rosner, and Tapias, D., (eds.), LREC. European Language Resources Association.Google Scholar
Resnik, P., and Yarowsky, D., 1999. Distinguishing systems and distinguishing senses: new evaluation methods for word sense disambiguation. Natural Language Engineering 5 (2): 113–133.CrossRefGoogle Scholar
Resnik, P. 2004. Exploiting hidden meanings: using bilingual text for monolingual annotation. In International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), pp. 283–299.Google Scholar
Rudnick, A., Liu, C., and Gasser, M., 2013. Hltdi: Cl-wsd using markov random fields for semeval-2013 task 10. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval-2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics, pp. 171–177.Google Scholar
Schmid, H., 1994. Probabilistic part-of-speech tagging using decision trees. In Proceedings of International Conference on New Methods in Language Processing, Volume 12, Manchester, UK, pp. 44–49.Google Scholar
Schütze, H. 1998. Automatic word sense discrimination. Computational Linguistics 24 (1): 97–123, March.Google Scholar
Silberer, C., and Ponzetto, S. P., 2010. Uhd: cross-lingual word sense disambiguation using multilingual co-occurrence graphs. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval-10), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 134–137.Google Scholar
Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., and Tufi, D. 2006. The jrc-acquis: a multilingual aligned parallel corpus with 20+ languages. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC-2006), pp. 2142–2147.Google Scholar
Tan, L., and Bond, F., 2013. Xling: matching query sentences to a parallel corpus using topic models for wsd. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval-2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics, pp. 167–170.Google Scholar
Van Gompel, M., 2010. Uvt-wsd1: a cross-lingual word sense disambiguation system. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval-2010), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 238–241.Google Scholar
Van Gompel, M., and van den Bosch, A., 2013. Wsd2: parameter optimisation for memory-based cross-lingual word-sense disambiguation. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval-2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics, pp. 183–187.Google Scholar
Vickrey, D., Biewald, L., Teyssier, M., and Koller, D. 2005. Word-sense disambiguation for machine translation. In EMNLP, pp. 771–778.Google Scholar
Vilariño, D., Balderas, C., Pinto, D., Rodríguez, M., and León, S., 2010. Fcc: modeling probabilities with giza++ for task #2 and #3 of semeval-2. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval-2010), Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 112–116.Google Scholar