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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2014

01.11.2014 | Regular Paper

A new semantic relatedness measurement using WordNet features

verfasst von: Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha, Abdelmajid Ben Hamadou

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2014

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Abstract

Computing semantic similarity/relatedness between concepts and words is an important issue of many research fields. Information theoretic approaches exploit the notion of Information Content (IC) that provides for a concept a better understanding of its semantics. In this paper, we present a complete IC metrics survey with a critical study. Then, we propose a new intrinsic IC computing method using taxonomical features extracted from an ontology for a particular concept. This approach quantifies the subgraph formed by the concept subsumers using the depth and the descendents count as taxonomical parameters. In a second part, we integrate this IC metric in a new parameterized multistrategy approach for measuring word semantic relatedness. This measure exploits the WordNet features such as the noun “is a” taxonomy, the nominalization relation allowing the use of verb “is a” taxonomy and the shared words (overlaps) in glosses. Our work has been evaluated and compared with related works using a wide set of benchmarks conceived for word semantic similarity/relatedness tasks. Obtained results show that our IC method and the new relatedness measure correlated better with human judgments than related works.
Vorheriger Artikel Spatial–temporal compression and recovery in a wireless sensor network in an underground tunnel environment
Nächster Artikel Finding peculiar compositions of two frequent strings with background texts

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The Amazon Mechanical Turk (MTurk) is an online labor market where workers are paid small amounts of money to complete small tasks. https://​www.​mturk.​com/​mturk/​welcome.
 
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marimba.d.umn.edu/cgi-bin/similarity/similarity.cgi.
 
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Metadaten
Titel
A new semantic relatedness measurement using WordNet features
verfasst von
Mohamed Ali Hadj Taieb
Mohamed Ben Aouicha
Abdelmajid Ben Hamadou
Publikationsdatum
01.11.2014
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2014
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-013-0672-4

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