Arabic word sense disambiguation: a review

Elayeb, Bilel

doi:10.1007/s10462-018-9622-6

Cited by 24 publications

(21 citation statements)

References 98 publications

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“…Further, Arabic-based measure Aldiery performs better than AWS S , also [31]). It is also interesting is to study the effect of the semantic gaps over NLP applications; for instances Question Answering similar to the work presented in [44], and word sense disambiguation [33,35] in the context of Arabic.…”

Section: Resultsmentioning

confidence: 99%

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Evaluating the Impact of Semantic Gaps on Estimating the Similarity using Arabic Wordnet

Helou¹

2020

Adv. sci. technol. eng. syst. j.

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Knowledge-based approach is wield used in various NLP applications. For example, to evaluate the semantic similarity between words, the semantic evidence in lexical ontologies (wordnets) is commonly used. The success of the English WordNet (EnWN) in this domain has inspired the creation of several wordnets in different languages, including the Arabic WordNet (ArWN). The English synsets have been extended to Arabic synsets through translation, which have introduced semantic gaps in ArWN structure. Therefore, compared to EnWN, ArWN has limited coverage in terms of lexical and semantic knowledge. This paper explores to what degree the richness of the wordnets' semantic structure influences the semantic evidence that can be used in wordnet-based applications, in particular the effect of filling the semantic gaps in ArWN. The paper studies the performance of applying English-based and Arabic-based similarity measures over ArWN. A set of experiments was performed by applying six path-based semantic similarity measures over Arabic benchmark dataset to investigate the usability and efficacy of the enriched structure of ArWN. The Performance measures, Person Correlation and Mean Square Error, are computed against and compared to human judgment benchmark. The obtained results demonstrate that the semantic similarity between words can be significantly improved when filling the semantic gaps. In addition, the experiment findings show that Arabic-based measures competitively perform well compared to the English-based measures. Further, ArWN enhanced structure is also available for public.

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Section: Resultsmentioning

confidence: 99%

“…In linguistics, philosophy and information theory, estimating the semantic similarity between concepts is extensively studied [2,15], which is a common and crucial task in many NLP applications, text summarization, word sense disambiguation, entailment, machine translation, among many others [33]- [6], [34,35].…”

Section: Wordnet-based Similarity Measuresmentioning

confidence: 99%

Evaluating the Impact of Semantic Gaps on Estimating the Similarity using Arabic Wordnet

Helou¹

2020

Adv. sci. technol. eng. syst. j.

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“…The Lesk overlap method cannot determine the physical length between two concepts (semantic similarity), especially between the concepts of nouns and verbs [40]. Hence, several studies have used the semantic similarity method proposed by Wu and Palmer [41] [42] in the maximization process of GA [36].…”

Section: 0 Related Workmentioning

confidence: 99%

Word sense disambiguation using hybrid swarm intelligence approach

et al. 2018

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Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.

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“…Knowledge-based WSD methods usually utilize language resources such as knowledge-graphs and dictionaries to solve the WSD problem. Knowledgebased WSD can be further subcategorized into graph-based approaches and gloss-based approaches [16]. Graph-based approaches utilize language knowledge-graphs, such as WordNet, to represent the sense by its surrounding tokens in that graph.…”

Section: Introductionmentioning

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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Arabic word sense disambiguation: a review

Cited by 24 publications

References 98 publications

Evaluating the Impact of Semantic Gaps on Estimating the Similarity using Arabic Wordnet

Evaluating the Impact of Semantic Gaps on Estimating the Similarity using Arabic Wordnet

Word sense disambiguation using hybrid swarm intelligence approach

Arabic Gloss WSD Using BERT

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