The Acquisition of Common Sense Knowledge by Being Told: An Application of NLP to Itself

  • Fernando Gomez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5039)


This paper shows how the knowledge of a semantic interpreter can be bootstrapped for other semantic interpretation tasks. Methods are described for automatically acquiring common sense knowledge and for applying this knowledge to noun sense disambiguation. Ordinary concepts are described by several plain English sentences that are parsed and semantically interpreted. The semantic interpreted sentences are stored under these concepts to be used for semantic interpretation tasks. This paper explains the description of the concepts, the interpretation of the sentences and two algorithms for noun sense disambiguation that use the acquired knowledge.


Ontological Category Semantic Role Semantic Interpretation Word Sense Disambiguation Head Noun 
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.
    Fellbaum, C.: English Verbs as a Semantic Net. In: Fellbaum, C. (ed.) WordNet: An electronic Lexical Database and some of its applications, pp. 69–104. MIT Press, Cambridge (1998)Google Scholar
  2. 2.
    Gomez, F.: Building verb predicates: A computational view. In: Proceedings of the 42nd Meeting of the Association for Computational Linguistics, ACL 2004, Barcelona, Spain, pp. 351–358 (2004)Google Scholar
  3. 3.
    Gomez, F.: Semantic Interpretation and the Upper-Level Ontology of WordNet. Journal of Intelligent Systems 16(2), 93–116 (2007)Google Scholar
  4. 4.
    Gomez, F.: An algorithm for aspects of semantic interpretation using an enhanced wordnet. In: Proceedings of the 2nd North American Meeting of the North American Association for Computational Linguistics, pp. 87–94 (2001)Google Scholar
  5. 5.
    Hahn, U., Marko, K.G.: An integrated dual learner for grammars and ontologies. Data & Knowledge Engineering 42, 273–291 (2003)CrossRefGoogle Scholar
  6. 6.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In: Proceedings of the 1986 ACM SIGDOC Conference, Toronto, pp. 24–26 (1986)Google Scholar
  7. 7.
    Liu, H., Singh, P.: ConceptNet - a practical commonsense reasoning tool-kit. BT Technology Journal 22, 211–226 (2004)CrossRefGoogle Scholar
  8. 8.
    Mihalcea, R.: Knowledge-based methods. In: Agirre, E., Edmonds., P. (eds.) Word Sense Disambiguation, pp. 107–127. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Mihalcea, R., Moldovan, D.: A method for word sense disambiguation of unrestricted texts. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, College Park, Maryland, pp. 152–158 (1999)Google Scholar
  10. 10.
    Miller, G.: Nouns in wordnet. In: Fellbaum, C. (ed.) WordNet: An electronic Lexical Database and some of its applications, pp. 23–46. MIT Press, Cambridge (1998)Google Scholar
  11. 11.
    Moldovan, D., Russ, V.: Logic form transformation of wordnet and its applicability to question answering. In: Proceedings of the 39th meeting of the ACL, Toulouse, France, pp. 402–409 (2001)Google Scholar
  12. 12.
    Quillian, M.: Semantic memory. In: Minsky, M. (ed.) Semantic Information Processing, pp. 216–270. MIT Press, Cambridge, Mass (1968)Google Scholar
  13. 13.
    Quine, V.: Word and Object. MIT Press, Cambridge (1960)zbMATHGoogle Scholar
  14. 14.
    Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)zbMATHGoogle Scholar
  15. 15.
    Wittgenstein, L.: Philosophical Investigations. Blackwell, Oxford (1958)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fernando Gomez
    • 1
  1. 1.School of EECSUniversity of Central FloridaOrlando

Personalised recommendations