Automatic extraction of semantic relationships for WordNet by means of pattern learning from Wikipedia
Metadatos
Title:
Automatic extraction of semantic relationships for WordNet by means of pattern learning from Wikipedia
Author:
Ruiz-Casado, María; Alfonseca Cubero, Enrique; Castells, Pablo
Entity:
UAM. Departamento de Ingeniería Informática
UAM Author:
Castells Azpilicueta, Pablo
Publisher:
Springer-Verlag Berlin Heidelberg
Date:
2005
Citation:
10.1007/11428817_7
Natural Language Processing and Information Systems: 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15-17, 2005. Proceedings. Lecture Notes in Computer Science, Volumen 3513. Springer 2005. 67-79.
ISSN:
0302-9743 (print); 1611-3349 (online)
ISBN:
978-3-540-26031-8 (print); 978-3-540-32110-1 (online)
DOI:
10.1007/11428817_7
Funded by:
This work has been sponsored by CICYT, project number TIC2002-01948.
Editor's Version:
http://dx.doi.org/10.1007/11428817_7
Subjects:
Database Management; Computer Communication Networks; Logics and Meanings of Programs; Information Storage and Retrieval; Artificial Intelligence; Mathematical Logic; Formal Languages; Informática
Note:
The final publication is available at Springer via http://dx.doi.org/10.1007/11428817_7
Proceedings of 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15-17, 2005.
Rights:
© Springer-Verlag Berlin Heidelberg 2005
Abstract:
This paper describes an automatic approach to identify lexical patterns which represent semantic relationships between concepts, from an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 1200 new relationships that did not appear in WordNet originally. The precision of these relationships ranges between 0.61 and 0.69, depending on the relation.
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