Paper Info
Title
Leveraging synonymy and polysemy to improve semantic similarity assessments based on intrinsic information content
Abstract
Semantic similarity measures based on the estimation of the information content (IC) of concepts are currently regarded as the state of the art. Calculating the IC in an intrinsic (i.e., ontology-based) way is particularly convenient due to its accuracy and lack of dependency on annotated corpora. Intrinsic IC calculation models estimate concept probabilities from the taxonomic knowledge (i.e., number of hyponyms and/or hypernyms of the concepts) modelled in an ontology. In this paper, we aim to improve the intrinsic calculation of the IC by leveraging not only the hyponyms and hypernyms of concepts, but also the explicit evidences of synonymy and polysemy that ontologies such as WordNet also model. Specifically, we propose a more accurate intrinsic estimation of the concepts’ probabilities in which the IC calculation relies. We evaluate the accuracy of our proposal through a set of comprehensive experiments in which our IC calculation model is tested on a variety of IC-based similarity measures and benchmarks. Experimental results show that our proposal obtains consistently good accuracies, which vary less across measures and benchmarks than the most prominent intrinsic IC calculation models available in the literature.
Year
DOI
Venue
2020
10.1007/s10462-019-09725-4
Artificial Intelligence Review
Keywords
Field
DocType
Information content, Ontology-based semantic similarity, Synonymy, Polysemy, WordNet
Ontology (information science),Semantic similarity,Ontology,Computer science,Synonym,Artificial intelligence,Natural language processing,WordNet,Instrumental and intrinsic value,Machine learning,Polysemy
Journal
Volume
Issue
ISSN
53
3
0269-2821
Citations 
PageRank 
References 
1
0.35
0
Authors
2
Name
Order
Citations
PageRank
Montserrat Batet189937.20
David Sánchez269033.01