CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

Extraction of Definitional Contexts using Lexical Relations

by Olga Acosta, Gerardo Sierra, Cesar Aguilar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 34 - Number 6
Year of Publication: 2011
Authors: Olga Acosta, Gerardo Sierra, Cesar Aguilar
10.5120/4121-5982

Olga Acosta, Gerardo Sierra, Cesar Aguilar . Extraction of Definitional Contexts using Lexical Relations. International Journal of Computer Applications. 34, 6 ( November 2011), 46-53. DOI=10.5120/4121-5982

@article{ 10.5120/4121-5982,
author = { Olga Acosta, Gerardo Sierra, Cesar Aguilar },
title = { Extraction of Definitional Contexts using Lexical Relations },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 34 },
number = { 6 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 46-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume34/number6/4121-5982/ },
doi = { 10.5120/4121-5982 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:20:46.213323+05:30
%A Olga Acosta
%A Gerardo Sierra
%A Cesar Aguilar
%T Extraction of Definitional Contexts using Lexical Relations
%J International Journal of Computer Applications
%@ 0975-8887
%V 34
%N 6
%P 46-53
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present a method for automatically extracting definitional contexts from restricted domains in Spanish. Definitional contexts are textual fragments where there is an implicit definition that can be identified by taking into account verbal patterns linking a term and its corresponding definition. Our interest is in definitional contexts with analytical definitions. Therefore, we focus on the extraction of textual fragments with a term and a hypernym. Then, hypernym is used for filtering non-relevant contexts by means of the occurrence frequency. It is assumed most frequent hypernyms have a higher probability of giving true definitional contexts than those less frequent hypernyms. We captured regularity in analytical definitions by means of a chunk grammar. This method achieves acceptable results in precision and recall compared with other works.

References
  1. Smith, E. 1988. Psychology of human thought. Cambridge University Press.
  2. Rosch, E. 1978. Principles of categorization. In Rosh E. and Lloyd B. (eds.). Cognition and Cognitive Science. Elsevier.
  3. Smith, E. and Medin, D. 1981. Categories and concepts. Harvard University Press. Cambridge, Mass.
  4. Murphy, G. 2002. The big book of concepts. MIT Press. Cambridge, Mass.
  5. Wilks, Y., Slator, B., and Guthrie, L. 1995. Electric Words: dictionaries, computers and meanings. MIT Press. Cambridge, Mass.
  6. Buitelaar, P., Cimiano, P. and Magnini, B. 2005. Ontology learning from text. IOS Press. Amsterdam.
  7. 7 Sierra, G., Alarcón, R., Medina, A. and Aguilar, C. 2003. Definitional Contexts Extraction from Specialised Texts. Proceedings: Language, Corpora and E-Learning, Frankfurt: Peter Lang Publish: 21-31.
  8. Gruber, T. R., A. 1993. Translation Approach to Portable Ontology Specifications, Knowledge Acquisition 5(2), 1993:199-220.
  9. Velardi, P., Fabriani, P., and Missikoff. 2001. Using Text Processing Techniques to Automatically enrich a Domain Ontology. In Proceedings of the ACM International Conference on Formal Ontology in Information Systems.
  10. Sowa, J. 2006. Categorization in Cognitive Computer Science. Handbook of Categorization in Cognitive Science, Elsevier.
  11. Laurence, S. and Margolis, E. 1999. Concepts and Cognitive Science. Concepts: Core Readings, Cambridge, Mass.: MIT Press.
  12. Malaisé, V., Zweigenbaum, P., and Bachimont, B. 2005. Mining Defining Contexts to Help Structuring Differential Ontologies. Terminology 11 (1). 21-53.
  13. Klavans, J., and Muresan, D. 2001. Evaluation of DEFINDER: A System to Mine Definitions from Consumer-Oriented Medical Texts. In Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’01).
  14. Sager, J. 1990. A Practical Course in Terminology Processing. Amsterdam/Philadelphia. John Benjamins.
  15. Smith, B. 2003. Ontology. In Floridi, L. (ed.). Blackwell Guide to the Philosophy of Computing and Information. Blackwell. Oxford: 155–166.
  16. Sierra, G., Alarcón, R., Aguilar C. and Bach, C. 2010. Definitional verbal patterns for semantic relation extraction. In Auger, A. and Barrière, C. (eds.). Probing Semantic Relations: Exploration and Identification in Specialized exts. Amsterdam/Philadelphia. John Benjamins, 73-96.
  17. Ortega, R., Montes, M., and Villaseñor, L. 2007. Using Lexical Patterns for Extracting Hyponyms from the Web. In: MICAI 2007. Advances in Artificial Intelligence. LNCS, Vol. 4827, pp.904-911. Springer, Berlin.
  18. Aguilar, C. 2009 Análisis Lingüístico de Definiciones en Contextos Definitorios. Tesis de doctorado, UNAM.
  19. Estopà, R. 2003. Extracció de terminologia: elements per a la construcció d'un SEACUSE. Doctoral Thesis. IULA-UPF. Barcelona, Spain.
  20. Vivaldi, J. 2004. Extracción de candidatos a términos mediante la combinación de estrategias heterogéneas. Doctoral Thesis. IULA-UPF. Barcelona, Spain.
  21. Litkowski, K. 2002. Digraph Analysis of Dictionary Prepositions Definitions. Proceedings of the SIGLEX/SENSEVAL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia. Association for Computational Linguistics.
  22. Jurafsky, D. and Martin, J. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. Prentice-Hall. New Jersey.
  23. Croft, W. and Cruse, A. 2004. Cognitive Linguistics. Cambridge University Press.
  24. Schmid, H. 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of International Conference of New Methods in Language. WEB Site: www.ims.uni-stuttgart.de~schmid.TreeTagger
  25. Bird, S., Klein, E. and Loper, E. 2009. Natural Language Processing whit Python. O'Reilly, Sebastropol, Cal.
Index Terms

Computer Science
Information Sciences

Keywords

Natural language processing Information extraction Conceptual extraction Lexical relations