A comprehensive review on Arabic word sense disambiguation for natural language processing applications

Document Type

Article

Source of Publication

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Publication Date

1-1-2022

Abstract

In communication, textual data are a vital attribute. In all languages, ambiguous or polysemous words' meaning changes depending on the context in which they are used. The ability to determine the ambiguous word's correct meaning is a Know-distill challenging task in natural language processing (NLP). Word sense disambiguation (WSD) is an NLP process to analyze and determine the correct meaning of polysemous words in a text. WSD is a computational linguistics task that automatically identifies the polysemous word's set of senses. Based on the context some word comes into view, WSD recognizes and tags the word to its correct priori known meaning. Semitic languages like Arabic have even more significant challenges than other languages since Arabic lacks diacritics, standardization, and a massive shortage of available resources. Recently, many approaches and techniques have been suggested to solve word ambiguity dilemmas in many different ways and several languages. In this review paper, an extensive survey of research works is presented, seeking to solve Arabic word sense disambiguation with the existing AWSD datasets. This article is categorized under: Algorithmic Development > Text Mining Technologies > Machine Learning.

ISSN

1942-4787

Publisher

Wiley

Disciplines

Computer Sciences | Linguistics

Scopus ID

85122897814

Indexed in Scopus

yes

Open Access

no

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