Nothing Special   »   [go: up one dir, main page]

Skip to main content

A Semantic Similarity Measurement Tool for WordNet-Like Databases

  • Conference paper
  • First Online:
Human Language Technology. Challenges for Computer Science and Linguistics (LTC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10930))

Included in the following conference series:

Abstract

The paper describes a new framework for computing the semantic similarity of words and concepts using WordNet-like databases. The main advantage of the presented approach is the ability to implement similarity measures as concise expressions in the embedded query language. The preliminary results of the use of the framework to model the semantic similarity of Polish nouns are reported.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Notes

  1. 1.

    A joint transitive hypernym of two synsets such that no other joint transitive hypernym of these synsets is placed below it within the hypernymy hierarchy.

  2. 2.

    Databases that are organized similarly to WordNet [4], called wordnets in the rest of the paper.

  3. 3.

    Through the JWI library [5].

  4. 4.

    We assume in the following examples that all commands are invoked in the Linux shell environment.

  5. 5.

    Interested readers can consult [11].

  6. 6.

    The synsets satisfying the condition empty(hypernym).

  7. 7.

    The pair środek dnia/południe is omitted in Table 3, since środek dnia occurs in neither PlWordNet 2.2 nor in PolNet 3.0.

  8. 8.

    In the case of information content-based measures.

  9. 9.

    With the exception of Pearson’s correlation coefficient for the Jiang-Conrath measure.

  10. 10.

    Polynomial kernels of degrees 2 and 3 were considered.

References

  1. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  3. Diedenhofen, B.: cocor: Comparing correlations, (Version 1.0-0) (2013). http://r.birkdiedenhofen.de/pckg/cocor/

  4. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA (1998)

    MATH  Google Scholar 

  5. Finlayson, M.A.: Java libraries for accessing the princeton wordnet: comparison and evaluation. In: Proceedings of the 7th Global Wordnet Conference, Tartu, Estonia, pp. 78–85 (2014)

    Google Scholar 

  6. Global WordNet Association: Global WordNet Grid (2012). http://globalwordnet.org/global-wordnet-grid/. Accessed 20 Sept 2015

  7. Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms, chap. 13, pp. 305–332. In: Fellbaum [4] (1998)

    Google Scholar 

  8. Horak, A., Pala, K., Rambousek, A., Povolny, M.: DEBVisDic - first version of new client-server wordnet browsing and editing tool. In: Sojka, P., et al. (eds.) Proceedings of the Third International WordNet Conference - GWC 2006. Masaryk University, Brno, Czech Republic (2005)

    Google Scholar 

  9. Isahara, H., Bond, F., Uchimoto, K., Utiyama, M., Kanzaki, K.: Development of the Japanese WordNet. In: Proceedings of the International Conference on Language Resources and Evaluation, LREC 2008, Marrakech, Morocco, 26 May–1 June 2008, European Language Resources Association (2008)

    Google Scholar 

  10. Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of 10th International Conference on Research in Computational Linguistics, ROCLING 1997 (1997)

    Google Scholar 

  11. Kubis, M.: A query language for wordnet-like lexical databases. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012. LNCS (LNAI), vol. 7198, pp. 436–445. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28493-9_46

    Chapter  Google Scholar 

  12. Kubis, M.: A tool for transforming wordnet-like databases. In: Vetulani, Z., Mariani, J. (eds.) LTC 2011. LNCS (LNAI), vol. 8387, pp. 343–355. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08958-4_28

    Chapter  Google Scholar 

  13. Kubis, M.: A semantic similarity measurement tool for WordNet-like databases. In: Vetulani, Z., Mariani, J. (eds.) Proceedings of the 7th Language and Technology Conference, pp. 150–154. Fundacja Uniwersytetu im. Adama Mickiewicza, Poznań, Poland, November 2015

    Google Scholar 

  14. Leacock, C., Chodorow, M.: Combining local context and wordnet similarity for word sense identification, chap. 11, pp. 265–283. In: Fellbaum [4] (1998)

    Google Scholar 

  15. Liaw, A., Wiener, M.: Classification and Regression by randomForest. R News 2(3), 18–22 (2002). http://CRAN.R-project.org/doc/Rnews/

    Google Scholar 

  16. Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 296–304. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)

    Google Scholar 

  17. Maziarz, M., Piasecki, M., Szpakowicz, S.: Approaching plWordNet 2.0. In: Proceedings of the 6th Global Wordnet Conference. Matsue, Japan, January 2012

    Google Scholar 

  18. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: e1071: Misc functions of the department of statistics (e1071), TU Wien, R package version 1.6-3 (2014). http://CRAN.R-project.org/package=e1071

  19. Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Lang. Cognit. Process. 6(1), 1–28 (1991)

    Article  Google Scholar 

  20. Paliwoda-Pękosz, G., Lula, P.: Measures of semantic relatedness based on wordnet. In: International Workshop For Ph.D. Students. Brno, Czech Republic (2009). ISBN: 978-80-214-3980-1

    Google Scholar 

  21. Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic relatedness for word sense disambiguation. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 241–257. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36456-0_24

    Chapter  Google Scholar 

  22. Pedersen, T.: Information content measures of semantic similarity perform better without sense-tagged text. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 329–332. Association for Computational Linguistics, Stroudsburg, PA, USA (2010)

    Google Scholar 

  23. Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet::Similarity: Measuring the Relatedness of Concepts. In: Demonstration Papers at HLT-NAACL 2004, pp. 38–41, HLT-NAACL-Demonstrations 2004, Association for Computational Linguistics, Stroudsburg, PA, USA (2004). http://dl.acm.org/citation.cfm?id=1614025.1614037

  24. Postma, M., Vossen, P.: What implementation and translation teach us: the case of semantic similarity measures in wordnets. In: Orav, H., Fellbaum, C., Vossen, P. (eds.) Proceedings of the Seventh Global Wordnet Conference, Tartu, Estonia, pp. 133–141 (2014)

    Google Scholar 

  25. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014). http://www.R-project.org/

  26. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)

    Article  Google Scholar 

  27. Resnik, P.: using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 1, IJCAI 1995, pp. 448–453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995)

    Google Scholar 

  28. Rubenstein, H., Goodenough, J.B.: Contextual correlates of synonymy. Commun. ACM 8(10), 627–633 (1965)

    Article  Google Scholar 

  29. Shima, H.: ws4j - WordNet Similarity for Java (2015). https://code.google.com/p/ws4j/. Accessed 28 Aug 2015

  30. Soria, C., Monachini, M., Vossen, P.: Wordnet-LMF: Fleshing out a standardized format for wordnet interoperability. In: Proceeding of the 2009 international workshop on Intercultural collaboration, pp. 139–146. ACM, New York, USA (2009)

    Google Scholar 

  31. Stevenson, M., Greenwood, M.A.: A semantic approach to IE pattern induction. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, pp. 379–386. Association for Computational Linguistics, Stroudsburg, PA, USA (2005)

    Google Scholar 

  32. Tengi, R.I.: Design and Implementation of the WordNet Lexical Database and Searching Software, chap. 4, pp. 105–127. In: Fellbaum [4] (1998)

    Google Scholar 

  33. Therneau, T., Atkinson, B., Ripley, B.: rpart: recursive partitioning and regression trees, R package version 4.1-8 (2014). http://CRAN.R-project.org/package=rpart

  34. Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S. Springer, New York (2002). https://doi.org/10.1007/978-0-387-21706-2. http://www.stats.ox.ac.uk/pub/MASS4. ISBN 0-387-95457-0

    Book  MATH  Google Scholar 

  35. Vetulani, Z., Kubis, M., Obrębski, T.: PolNet - Polish WordNet: Data and Tools. In: Calzolari, N., et al. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation, ELRA, Valletta, Malta, May 2010

    Google Scholar 

  36. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, ACL 1994, pp. 133–138. Association for Computational Linguistics, Stroudsburg, PA, USA (1994). https://doi.org/10.3115/981732.981751

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marek Kubis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kubis, M. (2018). A Semantic Similarity Measurement Tool for WordNet-Like Databases. In: Vetulani, Z., Mariani, J., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2015. Lecture Notes in Computer Science(), vol 10930. Springer, Cham. https://doi.org/10.1007/978-3-319-93782-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93782-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93781-6

  • Online ISBN: 978-3-319-93782-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics