A comprehensive review on Arabic word sense disambiguation for natural language processing applications. (18th January 2022)

• Record Type: Journal Article
• Authors: Kaddoura, Sanaa; D. Ahmed, Rowanda; D., Jude Hemanth
• Is Part Of: Wiley interdisciplinary reviews. Volume 12:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-18
• Subjects: Arabic language -- machine learning -- natural language processing -- word sense disambiguation -- WordNet; Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1447
• Languages: English
• ISSNs: 1942-4787

Abstract

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 Abstract : WSD Methodologies