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Academic Paper


Title: Recent advances in methods of lexical semantic relatedness – a survey
Author: Ziqi Zhang
Institution: University of Sheffield
Author: Anna Lisa Gentile
Institution: University of Sheffield
Author: Fabio Ciravegna
Institution: University of Sheffield
Linguistic Field: Computational Linguistics
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.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 19, Issue 4, which you can read on Cambridge's site or on LINGUIST .



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