Sense and Similarity: A Study of Sense-level Similarity Measures

In this paper, we investigate the difference between word and sense similarity measures and present means to converta state-of-the-art word similarity measure into a sense similarity measure. In order to evaluate the new measure, we create a special sense similarity dataset and re-rate an existing word similarity dataset using two different sense inventories from WordNet and Wikipedia. We discover that word-level measures were not able to differentiate between different senses of one word, while sense-level measures actually increase correlation when shifting to sense similarities. Sense-level similarity measures improve when evaluated with a re-rated sense-aware gold standard, while correlation with word-level similarity measures decreases.

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Conference:
Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), August 23-24, 2014 Dublin, Ireland
Date Issued:
2014
URN:
urn:nbn:de:hbz:464-20211025-162815-4
DOI:
10.3115/v1/S14-1004
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Erbs N, Gurevych I, Zesch T (2014) Sense and Similarity: A Study of Sense-level Similarity Measures. In: Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), pp. 30-39. Association for Computational Linguistics and Dublin City University. https://doi.org/10.3115/v1/S14-1004

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