Measuring conceptual distance using WordNet: the design of a metric for measuring semantic similarity

Persistent Link:
http://hdl.handle.net/10150/126645
Title:
Measuring conceptual distance using WordNet: the design of a metric for measuring semantic similarity
Author:
Lewis, William D.
Affiliation:
University of Arizona
Publisher:
University of Arizona Linguistics Circle
Journal:
Coyote Papers: Working Papers in Linguistics, Language in Cognitive Science
Issue Date:
2001
URI:
http://hdl.handle.net/10150/126645
Abstract:
This paper describes the development of a metric for measuring the semantic distance or similarity of words using the WordNet lexical database. Such a metric could be of use in development of search engines and text retrieval systems, tasks for which the richness of natural language can cause difficulty. Further, such a metric can prove invaluable to psycholinguists who wish to study lexical semantic similarity or speech errors (specifically malapropisms). The paper first explores an adjusted distance metric, a la Rada et al. 1989, and the problems such a metric presents. Additional analysis shows that adjustments can be made to such a distance metric using density calculations, both based on depth within the network and based on local density. The paper ends with a discussion about automating the task of identifying regions within the semantic space over which density calculations can be made.
Type:
text; Article
Language:
en_US
ISSN:
0894-4539

Full metadata record

DC FieldValue Language
dc.contributor.authorLewis, William D.en_US
dc.date.accessioned2011-03-31T18:04:01Z-
dc.date.available2011-03-31T18:04:01Z-
dc.date.issued2001-
dc.identifier.issn0894-4539-
dc.identifier.urihttp://hdl.handle.net/10150/126645-
dc.description.abstractThis paper describes the development of a metric for measuring the semantic distance or similarity of words using the WordNet lexical database. Such a metric could be of use in development of search engines and text retrieval systems, tasks for which the richness of natural language can cause difficulty. Further, such a metric can prove invaluable to psycholinguists who wish to study lexical semantic similarity or speech errors (specifically malapropisms). The paper first explores an adjusted distance metric, a la Rada et al. 1989, and the problems such a metric presents. Additional analysis shows that adjustments can be made to such a distance metric using density calculations, both based on depth within the network and based on local density. The paper ends with a discussion about automating the task of identifying regions within the semantic space over which density calculations can be made.en_US
dc.language.isoen_USen_US
dc.publisherUniversity of Arizona Linguistics Circleen_US
dc.titleMeasuring conceptual distance using WordNet: the design of a metric for measuring semantic similarityen_US
dc.typetexten_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Arizonaen_US
dc.identifier.journalCoyote Papers: Working Papers in Linguistics, Language in Cognitive Scienceen_US
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