LREC 2000 - Papers

LREC 2000 2^nd International Conference on Language Resources & Evaluation

Conference Papers

Papers by paper title: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Papers by ID number: 1-50, 51-100, 101-150, 151-200, 201-250, 251-300, 301-350, 351-377.

List of all papers and abstracts.

Previous Paper Next Paper

Title Extraction of Semantic Clusters for Terminological Information Retrieval from MRDs

Authors Sierra Gerardo (Instituto de Ingenieria, UNAM Apdo. Postal 70-472 Mexico 04510, D.F., email: gsm@pumas.iingen.unam.mx)
McNaught John (Centre for Computational Linguistics, UMIST P.O.Box 88 Manchester, U.K., M60 1QD email: jock@ccl.umist.ac.uk)

Keywords Clustering, Definitions, Dictionaries, Information Retrieval, Lexicography, Natural Language Processing, Ontologies, Semantics, Terminology

Session Session TP1 - Terminology

Abstract This paper describes a semantic clustering method for data extracted from machine readable dictionaries (MRDs) in order to build a terminological information retrieval system that finds terms from descriptions of concepts. We first examine approaches based on ontologies and statistics, before introducing our analogy-based approach that lets us extract semantic clusters by aligning definitions from two dictionaries. Evaluation of the final set of clusters for a small set of definitions demonstrates the utility of our approach.

face="Verdana">

Title	Extraction of Semantic Clusters for Terminological Information Retrieval from MRDs
Authors	Sierra Gerardo (Instituto de Ingenieria, UNAM Apdo. Postal 70-472 Mexico 04510, D.F., email: gsm@pumas.iingen.unam.mx) McNaught John (Centre for Computational Linguistics, UMIST P.O.Box 88 Manchester, U.K., M60 1QD email: jock@ccl.umist.ac.uk)
Keywords	Clustering, Definitions, Dictionaries, Information Retrieval, Lexicography, Natural Language Processing, Ontologies, Semantics, Terminology
Session	Session TP1 - Terminology
Abstract	This paper describes a semantic clustering method for data extracted from machine readable dictionaries (MRDs) in order to build a terminological information retrieval system that finds terms from descriptions of concepts. We first examine approaches based on ontologies and statistics, before introducing our analogy-based approach that lets us extract semantic clusters by aligning definitions from two dictionaries. Evaluation of the final set of clusters for a small set of definitions demonstrates the utility of our approach.