Abstract
Tendencies in Natural Language Processing (NLP) systems go towards the study of the semantic knowledge that can be extracted from the words within a context. For this reason, semantic information addition is necessary for the definition of new NLP strategies.
In this paper, a method oriented to pronominal anaphora resolution in Spanish is presented. This method is based on two ontologies, On and Ov, that define semantic concept branches for nouns and verbs respectively. Also, the method uses two relation sets in order to solve the anaphora. These sets are formed by semantic patterns that define the compatibility between a subject and its verb and verb and its complement, using the ontological concepts of their heads.
The semantic behaviour provided by these patterns allows the identification of the noun phrase antecedents of a Spanish personal pronoun. This antecedent has the best compatibility ratio among all proposed antecedents of the anaphoric pronoun. This compatibility ratio is measured using the relation level between concepts in the ontological branch of each pair member.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
B. Baldwin. CogNIAC: high precision coreference with limited knowledge and linguistic resources. In ACL/EACL workshop on Operational factors in practical, robust anaphor resolution, 1997.
D. Clark. Natural Language, relevancy ranking and common sense. IEEE Intelligent Systems, 14(4):17–19, 1999.
A. Ferrández, M. Palomar, and L. Moreno. Anaphora resolution in unrestricted texts with partial parsing. In 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Lingustics, pages 385–391, 1998.
R. Gaizauskas, T. Wakao, K. Humphreys, H. Cunningham, and Y. Wilks. Description of the LaSIE system as used for MUC-6. In Sixth Message Understanding Conference, pages 207–220, 1995.
J. Hobbs. Resolving pronoun references. Lingua, 44:311–338, 1997.
K. Humphreys, R. Gaizauskas, S. Azzam, C. Huyck, B. Mitchell, H. Cunningham, and Y. Wilks. Description of the LaSIE-II System as Used for MUC-7. In Seventh Message Understanding Conference, 1998.
C. Kennedy and B. Boguraev. Anaphora for everyone: pronominal anaphora resolution without a parser. In 16th International Conference on Computational Linguistics volume I, pages 113–118, 1996.
S. Lappin and H. Leass. An algorithm for pronominal anaphora resolution. Computational Linguistics, 20(4):535–561, 1994.
R. Mitkov and M. Stys. Robust reference resolution with limited knowledge: high precision genre-specific approach for English and Polish. In Recent Advances in Natural Language Resolution, 1997.
L. Moreno, F. Andrés, and M. Palomar. Incorporar Restricciones Semánticas en el Análisis Sintáctico: IRSAS. Procesamiento del Lenguaje Natural, 12, 1992.
P. Vossen. Euro WordNet: Building a Multilingual Database with WordNets for European Languages. The ELRA Newwsletter. K.Choukri, D.Fry, M. Nilsson (des). ISSN:1026-8200, 3(1), 1998.
M. Saiz-Noeda, J. Peral, and A. Suárez. Semantic Compatibility Techniques for Anaphora Resolution. In International Conference on Artificial and Computational Intelligence for Decision, Control and Automation in Engineering and Industrial Applications, March 2000.
A. Suárez, M. Saiz-Noeda, and M. Palomar. A method of restricted knowledge acquisition from WordNet. In Third international conference on knowledge-based intelligent information engineering systems, August 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saiz-Noeda, M., Palomar, M. (2000). Semantic Knowledge-Driven Method to Solve Pronominal Anaphora in Spanish Texts. In: Christodoulakis, D.N. (eds) Natural Language Processing — NLP 2000. NLP 2000. Lecture Notes in Computer Science(), vol 1835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45154-4_19
Download citation
DOI: https://doi.org/10.1007/3-540-45154-4_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67605-8
Online ISBN: 978-3-540-45154-9
eBook Packages: Springer Book Archive