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Recent advances in methods of lexical semantic relatedness – a survey

Published online by Cambridge University Press:  04 May 2012

ZIQI ZHANG ,
ANNA LISA GENTILE  and
FABIO CIRAVEGNA
ZIQI ZHANG
Affiliation:
Department of Computer Science, University of Sheffield 211 Portobello, Regent Court, Sheffield, UK, S1 4DP e-mail: z.zhang@dcs.shef.ac.uk, a.l.gentile@dcs.shef.ac.uk, f.ciravegna@dcs.shef.ac.uk
ANNA LISA GENTILE
Affiliation:
Department of Computer Science, University of Sheffield 211 Portobello, Regent Court, Sheffield, UK, S1 4DP e-mail: z.zhang@dcs.shef.ac.uk, a.l.gentile@dcs.shef.ac.uk, f.ciravegna@dcs.shef.ac.uk
FABIO CIRAVEGNA
Affiliation:
Department of Computer Science, University of Sheffield 211 Portobello, Regent Court, Sheffield, UK, S1 4DP e-mail: z.zhang@dcs.shef.ac.uk, a.l.gentile@dcs.shef.ac.uk, f.ciravegna@dcs.shef.ac.uk
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Abstract

Measuring lexical semantic relatedness is an important task in Natural Language Processing (NLP). It is often a prerequisite to many complex NLP tasks. Despite an extensive amount of work dedicated to this area of research, there is a lack of an up-to-date survey in the field. This paper aims to address this issue with a study that is focused on four perspectives: (i) a comparative analysis of background information resources that are essential for measuring lexical semantic relatedness; (ii) a review of the literature with a focus on recent methods that are not covered in previous surveys; (iii) discussion of the studies in the biomedical domain where novel methods have been introduced but inadequately communicated across the domain boundaries; and (iv) an evaluation of lexical semantic relatedness methods and a discussion of useful lessons for the development and application of such methods. In addition, we discuss a number of issues in this field and suggest future research directions. It is believed that this work will be a valuable reference to researchers of lexical semantic relatedness and substantially support the research activities in this field.

Type
Articles
Information
Natural Language Engineering , Volume 19 , Issue 4 , October 2013 , pp. 411 - 479
Copyright
Copyright © Cambridge University Press 2012 

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