SEMANTIC MEASURES BASED ON WORDNET USING MULTIPLE INFORMATION SOURCES

Mamoun Abu Helou, Adnan Abid

Abstract

Recognizing semantic similarity between words is a generic problem for many applications of computational linguistics and artificial intelligence, such as text retrieval, classification and clustering. In this paper we investigate a new approach for measuring semantic similarity that combines methods of existing approaches that use different information sources in their similarity calculations namely, shortest path length between compared words, depth in the taxonomy hierarchy, information content, semantic density of compared words, and the gloss of words. We evaluate our measure against a benchmark set of human similarity ratings and the results show that our approach demonstrates better semantic measures as compared to the existing approaches.

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Paper Citation


in Harvard Style

Abu Helou M. and Abid A. (2010). SEMANTIC MEASURES BASED ON WORDNET USING MULTIPLE INFORMATION SOURCES . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 500-503. DOI: 10.5220/0003101905000503


in Bibtex Style

@conference{kdir10,
author={Mamoun Abu Helou and Adnan Abid},
title={SEMANTIC MEASURES BASED ON WORDNET USING MULTIPLE INFORMATION SOURCES },
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={500-503},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003101905000503},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - SEMANTIC MEASURES BASED ON WORDNET USING MULTIPLE INFORMATION SOURCES
SN - 978-989-8425-28-7
AU - Abu Helou M.
AU - Abid A.
PY - 2010
SP - 500
EP - 503
DO - 10.5220/0003101905000503