Automatic Acquisition of Semantic Relationships from Morphological Relatedness

  • Delphine Bernhard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)


Semantic relationships like specialisation can be acquired either by word-external methods relying on the context or word-internal methods based on lexical structure. Word segments are thus a relevant cue for the automatic acquisition of semantic relationships. We have developed an unsupervised method for morphological segmentation devised for this objective. Semantic relationships are deduced from specific morphological structures based on the segments discovered. Evaluation of the validity of the semantic relationships inferred is performed against WordNet and the NCI Thesaurus.


Semantic Relation Related Word Semantic Relationship Semantic Link Automatic Acquisition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hearst, M.A.: Automatic Acquisition of Hyponyms from Large Text Corpora. In: Proceedings of the Fourteenth International Conference on Computational Linguistics, Nantes, France, July 1992, pp. 539–545 (1992)Google Scholar
  2. 2.
    Grabar, N., Zweigenbaum, P.: Lexically-based terminology structuring: a feasibility study. In: Proceedings of the LREC Workshop on Using Semantics for Information Retrieval and Filtering, Las Palmas, Canaries, pp. 73–77 (2002)Google Scholar
  3. 3.
    Ibekwe-SanJuan, F.: Terminological variation, a means of identifying research topics from texts. In: Proceedings of the Joint International Conference on Computational Linguistics (COLING-ACL 1998), Montréal, Québec, pp. 564–570 (1998)Google Scholar
  4. 4.
    Bodenreider, O., Burgun, A., Rindflesch, T.C.: Lexically-suggested hyponymic relations among medical terms and their representation in the UMLS. In: Actes de la Quatrième rencontre Terminologie et Intelligence Artificielle (TIA 2001), Nancy, France, pp. 11–21 (2001)Google Scholar
  5. 5.
    Ibekwe-SanJuan, F.: Inclusion lexicale et proximité sémantique entre termes. In: Actes des Sixièmes rencontres Terminologie et Intelligence Artificielle (TIA 2005), Rouen, France, pp. 45–57 (2005)Google Scholar
  6. 6.
    Daille, B.: Conceptual structuring through term variations. In: Bond, F., Korhonen, A., MacCarthy, D., Villacicencio, A. (eds.) Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, pp. 9–16 (2003)Google Scholar
  7. 7.
    Light, M.: Morphological cues for lexical semantics. In: Proceedings of the 34th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 25–31 (1996)Google Scholar
  8. 8.
    Namer, F., Zweigenbaum, P.: Acquiring meaning for French medical terminology: contribution of morphosemantics. In: Proceedings of Medinfo, San Francisco, CA, vol. 11, pp. 535–539 (2004)Google Scholar
  9. 9.
    Zweigenbaum, P., Grabar, N.: Liens morphologiques et structuration de terminologie. In: Actes de IC 2000: Ingénierie des Connaissances, pp. 325–334 (2000)Google Scholar
  10. 10.
    Claveau, V., L’Homme, M.C.: Structuring Terminology by Analogy-Based Machine Learning. In: Proceedings of the 7th International Conference on Terminology and Knowledge Engineering, TKE 2005 (2005)Google Scholar
  11. 11.
    Schwab, D., Lafourcade, M., Prince, V.: Extraction semi-supervisée de couples d’antonymes grâce à leur morphologie. In: Actes de TALN 2005, pp. 73–82 (2005)Google Scholar
  12. 12.
    Bernhard, D.: Unsupervised Morphological Segmentation Based on Segment Predictability and Word Segments Alignment. In: Proceedings of the Pascal Challenges Workshop on the Unsupervised Segmentation of Words into Morphemes, Venice, Italy, pp. 19–23 (2006)Google Scholar
  13. 13.
    Creutz, M., Lagus, K.: Inducing the Morphological Lexicon of a Natural Language from Unannotated Text. In: Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2005), Espoo, Finland (2005)Google Scholar
  14. 14.
    Bordag, S.: Unsupervised Knowledge-Free Morpheme Boundary Detection. In: Proceedings of RANLP (Recent Advances in Natural Language Processing), Borovets, Bulgaria (2005)Google Scholar
  15. 15.
    Kurimo, M., Creutz, M., Varjokallio, M., Arisoy, E., Saraclar, M.: Unsupervised segmentation of words into morphemes – Challenge 2005: An Introduction and Evaluation Report. In: Proceedings of the Pascal Challenges Workshop on the Unsupervised Segmentation of Words into Morphemes, Venice, Italy, pp. 1–11 (2006)Google Scholar
  16. 16.
    Baroni, M., Bernardini, S.: BootCaT: Bootstrapping Corpora and Terms from the Web. In: Lino, M.T., Xavier, M.F., Ferreira, F., Costa, R., Silva, R. (eds.) Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC), Lisbon, Portugal, pp. 1313–1316 (2004)Google Scholar
  17. 17.
    National Cancer Institute, Office of Communications and Center for Bioinformatics: NCI Thesaurus (2006) (Online) (accessed March 23, 2006),
  18. 18.
    Miller, G.A.: WordNet: a lexical database for English. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  19. 19.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  20. 20.
    Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments & Computers 28(2), 203–208 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Delphine Bernhard
    • 1
  1. 1.TIMC-IMAGInstitut de l’Ingénierie et de l’Information de Santé, Faculté de MédecineLa TroncheFrance

Personalised recommendations