Abstract
In this paper, we introduce a WordNet-based measure of semantic relatedness by combining the structure and content of WordNet with co–occurrence information derived from raw text. We use the co–occurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the co–occurrence information.
| Original language | English (US) |
|---|---|
| Pages | 1-8 |
| Number of pages | 8 |
| State | Published - 2006 |
| Externally published | Yes |
| Event | 2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together - Trento, Italy Duration: Apr 4 2006 → … |
Conference
| Conference | 2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together |
|---|---|
| Country/Territory | Italy |
| City | Trento |
| Period | 4/4/06 → … |
Bibliographical note
Publisher Copyright:© EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together. All rights reserved.