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A generalized vector space model for text retrieval based on semantic relatedness (2009)
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| Venue: | IN: PROC. OF THE 12TH EACL (SRW |
| Citations: | 13 - 3 self |
BibTeX
@INPROCEEDINGS{Tsatsaronis09ageneralized,
author = {George Tsatsaronis and Vicky Panagiotopoulou},
title = { A generalized vector space model for text retrieval based on semantic relatedness},
booktitle = {IN: PROC. OF THE 12TH EACL (SRW},
year = {2009},
pages = {70--78},
publisher = {}
}
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Abstract
Generalized Vector Space Models (GVSM) extend the standard Vector Space Model (VSM) by embedding additional types of information, besides terms, in the representation of documents. An interesting type of information that can be used in such models is semantic information from word thesauri like WordNet. Previous attempts to construct GVSM reported contradicting results. The most challenging problem is to incorporate the semantic information in a theoretically sound and rigorous manner and to modify the standard interpretation of the VSM. In this paper we present a new GVSM model that exploits WordNet’s semantic information. The model is based on a new measure of semantic relatedness between terms. Experimental study conducted in three TREC collections reveals that semantic information can boost text retrieval performance with the use of the proposed GVSM.
Keyphrases
generalized vector space model semantic relatedness text retrieval semantic information new gvsm model trec collection reveals challenging problem wordnet semantic information interesting type experimental study word thesaurus rigorous manner standard interpretation standard vector space model previous attempt text retrieval performance additional type new measure
