Word sense disambiguation for Arabic text using Wikipedia and Vector Space Model

Alian, Marwah; Awajan, Arafat; Alkouz, Akram

doi:10.1007/s10772-016-9376-y

Cited by 13 publications

(4 citation statements)

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“…The Word2Vec model whose word dimensions vary from 200-300 dimension. The effect of word sense disambiguation in natural language requirements is discussed by the authors [4]. The discussed methodology analyzes the impact of lexical ambiguity on the user statement identified by context detection.…”

Section: Related Workmentioning

confidence: 99%

Effect of Supervised Sense Disambiguation Model Using Machine Learning Technique and Word Embedding in Word Sense Disambiguation

Rupesh Mahajan,

2024

jes

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Natural language processing includes a subfield called word sense disambiguation, which focuses mostly on words that might have several meanings. Polysemous terms are also referred to as confusing phrases in some circles. The performance of word sense disambiguation depends on how effectively the ambiguous word is recognized by the machine. The discussed word embedding model for the ambiguous words represents the words from the document space to vector space with no data loss. The most identified challenge of ambiguous word representation is the features. The selection and representation of ambiguous words with respect to the features is the tedious task of word embedding. The discussed word embedding model uses countable features of available context for disambiguation. The proposed model is implemented for ambiguous words with context information. The available context of ambiguous/polysemous words is used for disambiguation. The unavailability of the context is the challenge in this model. The Recurrent Neural Network with Large Small Term Memory is used for the classification. The output of the RNN-LSTM is the sense values which are further mapped with the freely available lexical resource WordNet for retrieving the correct sense(meaning).

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Section: Related Workmentioning

confidence: 99%

Effect of Supervised Sense Disambiguation Model Using Machine Learning Technique and Word Embedding in Word Sense Disambiguation

Rupesh Mahajan,

2024

jes

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“…In Alian et al (2016b), the authors extend their work in English and compare their experiments with AWSD using Wikipedia texts. The experiments showed the best results in getting the first paragraph from the used Wikipedia texts for each sense.…”

Section: Arabic Wsd Datasetsmentioning

confidence: 99%

“…When it comes to the Arabic language, lack of linguistic resources like electronic dictionaries and the absence of standard collections for evaluation (Boudabous et al, 2013;Bounhas, 2012;Bounhas et al, 2011) are the most prominent problem the language suffers from. So, most of the researchers who started to work in WSD for Arabic language adopted knowledge-based approaches using one or more sources of knowledge in developing their disambiguation methods (Alian et al, 2016b;Bouhriz et al, 2016;Hadni et al, 2016;Menai & Alsaeedan, 2012;Pinto et al, 2007;Zouaghi et al, 2011). Some AWSD knowledge-based approaches have exploited several semantic similarity measures that benefitted from WordNet's semantic connections between word senses while other AWSD knowledge-based approaches depend on gloss overlap or, they are in other words, Lesk algorithm-based knowledge-based approaches (Lesk, 1986).…”

Section: Knowledge-based Arabic Wsd Approachesmentioning

confidence: 99%

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

Kaddoura

Ahmed

Hemanth

2022

WIREs Data Min & Knowl

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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.

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“…Kadim and Lazrek (2016) proposed a hypothesis for the selection results for a POS tagging implemented for the Arabic language and presented numerous cases where the morphosyntactic state of a word depends on the states of the subsequent words. Alian, Awajan, and Al-Kouz (2016) et al introduced a vector space to Arabic WSD, utilizing Wikipedia as a lexical resource for disambiguation. This approach was also tested on English words for improved generalizability.…”

Section: Examples Of How To Pick a Good American President From 2016 ...mentioning

confidence: 99%

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Piper

Rodger

2022

International Journal on Semantic Web and Information Systems

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We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our findings based on a website (www.presidentselect.com) that we developed, where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis is used to determine underlying word instances for elected and successful outputs. We also utilize our web site results to longitudinally extend these findings for decision making of potential election fraud detection in the 2020 Presidential election, utilizing Benford’s Law. Our results shed light on these phenomenon and provide new insights into the word sense disambiguation literature.

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Word sense disambiguation for Arabic text using Wikipedia and Vector Space Model

Cited by 13 publications

References 12 publications

Effect of Supervised Sense Disambiguation Model Using Machine Learning Technique and Word Embedding in Word Sense Disambiguation

Effect of Supervised Sense Disambiguation Model Using Machine Learning Technique and Word Embedding in Word Sense Disambiguation

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

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

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