Arabic Word Sense Disambiguation Using Wikipedia

Alian, Marwah; Awajan, Arafat Awajan; Alkouz, Akram

doi:10.21700/ijcis.2016.108

Cited by 12 publications

(5 citation statements)

References 10 publications

(11 reference statements)

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“…The work in Alian et al (2016a) is another approach, adopting the closest context to disambiguate Arabic polysemous words using Wikipedia as a lexical resource. Arabic WordNet ontology is a considerable knowledge resource in AWSD.…”

Section: Knowledge-based Arabic Wsd Approachesmentioning

confidence: 99%

“…The work in Alian et al (2016a) is another approach that adopted the closest context and cosine similarity after implementing the sentences by VSM to disambiguate Arabic polysemous words and using Wikipedia as a lexical resource. The authors computed the words' frequencies and their TF-IDF weight in VSM which shown results better than using raw frequencies.…”

Section: Contains Experimental Examplesmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Knowledge-based Arabic Wsd Approachesmentioning

confidence: 99%

Section: Contains Experimental Examplesmentioning

confidence: 99%

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

Kaddoura

Ahmed

Hemanth

2022

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Concepts with high match to the context were selected as the correct sense. In [19], the authors used AWN to generate a list of words that can be potentially ambiguous. For each ambiguous word, they found Wikipedia articles that correspond to the different senses of those words.…”

Section: Related Workmentioning

confidence: 99%

Arabic Gloss WSD Using BERT

2021

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Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

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“…Alian et al [5], relied on Arabic Wikipedia to extract the different meanings of the ambiguous word. They have applied Vector Space Model as a mathematical representation for documents.…”

Section: Word Sense Disambiguation: State Of the Artmentioning

confidence: 99%

A Framework for the Construction of a Historical Dictionary for Arabic

Laatar¹,

Aloulou²,

Belguith³

2019

RCS

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Arabic is one of the oldest Semitic languages in the world. But despite its rich historical heritage, Arabic is still bereft of a historical dictionary which traces the first use of its words and the evolution of their meanings and structures. Therefore, creating such a dictionary is of a great importance for the Arab world as it bridges the gap between its present and its past. This task should undergo several stages and requires a lot of effort. In this paper, we present our framework to help the linguists create a historical dictionary for Arabic. For this aim, we propose a platform which helps to trace the evolution of the meanings of a given word throughout time. The developed system allows the user to extract the meaning of an Arabic word according to the historical period in which it appeared. It also provides information about the oldest date of use of the word with a textual example in which it first appeared, and the first place where the word was used.

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Arabic Word Sense Disambiguation Using Wikipedia

Cited by 12 publications

References 10 publications

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

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

Arabic Gloss WSD Using BERT

A Framework for the Construction of a Historical Dictionary for Arabic

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