Skip to main content
SpringerLink
Log in
Book cover

International Conference on Model and Data Engineering

MEDI 2016: Model and Data Engineering pp 346–357Cite as

Word Similarity Based on Domain Graph

  • Conference paper
  • First Online:

  • 623 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9893))

Abstract

In this work we propose a new formalization for word similarity. Assuming that each word corresponds to unit of semantics, called synset, with categorical features, called domain, we construct a domain graph of a synset which is all the hypernyms which belong to the domain of the synset. Here we take an advantage of domain graphs to reflect semantic aspect of words. In experiments we show how well the domain graph approach goes well with word similarity. Then we extend sentence similarity (or Semantic Textual Similarity) independent of Bag-of-Words.

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.

    http://wn-similarity.sourceforge.net/, http://www.nltk.org/.

  2. 2.

    Sometimes this is called a ring.

  3. 3.

    There are 45 Lexicographer Files based on syntactic category and logical groupings. They contain synsets during WordNet development. There is another approach WordNet Domains which is a lexical resource created in a semi-automatic way by augmenting WordNet with domain labels. To each synset, there exists at least one semantic domain label annotated by hands from 200 labels [1].

  4. 4.

    https://code.google.com/archive/p/ws4j/.

  5. 5.

    It includes many short sentences extracted at more than 500 Twitter sites from April 24, 2013 to May 3, 2013. The corpus contain 17,790 pairs of sentences divided into 13,063 pairs for training and 4,727 pairs for development. And there are 972 pairs included for test. We examine these 13,063 pairs for training and the 972 pairs for test.

  6. 6.

    http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/.

References

  1. Bentivogli, L., Forner, P., Magnini, B., Pianta, E.: Revising WordNet domains hierarchy: semantics, coverage, and balancing. In: COLING 2004 Workshop on “Multilingual Linguistic Resources”, pp. 101–108 (2004)

    Google Scholar 

  2. Chang, C.C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  3. Cohen, E., et al.: Finding interesting associations without support pruning. IEEE Trans. Knowl. Data Eng. 13(1), 64–78 (2001)

    Article  Google Scholar 

  4. Das, D., Smith, N.A.: Paraphrase identification as probabilistic quasi-synchronous recognition. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 1. Association for Computational Linguistics, pp. 468–476 (2009)

    Google Scholar 

  5. Eyecioglu, A., Keller, B.: ASOBEK: twitter paraphrase identification with simple overlap features and SVMs. In: Proceedings of SemEval (2015)

    Google Scholar 

  6. Finkeltsein, L., et al.: Placing search in context: the concept revisited. In: Proceedings of the 10th International Conference on World Wide Web. ACM, pp. 406–414 (2001)

    Google Scholar 

  7. Finlayson, M.A.: Java libraries for accessing the Princeton WordNet: comparison and evaluation. In: Proceedings of the 7th Global Wordnet Conference, Tartu, Estonia (2014)

    Google Scholar 

  8. Guo, W., Diab, M.: Modeling sentences in the latent space. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1. Association for Computational Linguistics, pp. 864–872 (2012)

    Google Scholar 

  9. Konaka, F., Miura, T.: Textual similarity for word sequences. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds.) SISAP 2015. LNCS, vol. 9371, pp. 244–249. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25087-8_23

    Chapter  Google Scholar 

  10. Li, Y., et al.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18(8), 1138–1150 (2006)

    Article  Google Scholar 

  11. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  12. Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. (CSUR) 41(2), 10 (2009)

    Article  Google Scholar 

  13. Richens, T.: Anomalies in the WordNet verb hierarchy. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1. Association for Computational Linguistics, pp. 729–736 (2008)

    Google Scholar 

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

    Article  Google Scholar 

  15. Xu, W., Callison-Burch, C., Dolan, W.B.: SemEval-2015 task 1: paraphrase and semantic similarity in Twitter (PIT). In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval) (2015)

    Google Scholar 

  16. Yang, D., Powers, D.M.W.: Verb similarity on the taxonomy of WordNet. Masaryk University (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Advanced Sciences, Hosei University, 3-7-2 KajinoCho, Koganei, Tokyo, 184–8584, Japan

    Fumito Konaka & Takao Miura

Authors
  1. Fumito Konaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takao Miura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fumito Konaka .

Editor information

Editors and Affiliations

  1. LIAS/ISAE-ENSMA , Futuroscope Chasseneuil, France

    Ladjel Bellatreche

  2. Department of Information Systems and Computation, Universitat Politècnica de València, Valencia, Spain

    Óscar Pastor

  3. University of Almería , Almería, Spain

    Jesús M. Almendros Jiménez

  4. IRIT / ENSEIHT , Toulouse, France

    Yamine Aït-Ameur

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Konaka, F., Miura, T. (2016). Word Similarity Based on Domain Graph. In: Bellatreche, L., Pastor, Ó., Almendros Jiménez, J., Aït-Ameur, Y. (eds) Model and Data Engineering. MEDI 2016. Lecture Notes in Computer Science(), vol 9893. Springer, Cham. https://doi.org/10.1007/978-3-319-45547-1_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45547-1_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45546-4

  • Online ISBN: 978-3-319-45547-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation