A Hybrid Approach for Arabic Word Sense Disambiguation

Zouaghi, Anis; Merhbène, Laroussi; Zrigui, Mounir

doi:10.1142/s1793840612400090

Cited by 22 publications

(7 citation statements)

References 2 publications

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“…They used AWN as a source for the word gloss. The authors of [22] combined unsupervised and knowledge-based approaches. They used a string matching algorithm to match the root of salient words that appear in many contexts of a particular sense with the gloss definition.…”

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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Section: Related Workmentioning

confidence: 99%

Arabic Gloss WSD Using BERT

2021

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“…The method proposed by Zouaghi et al [31] is a hybrid method that combines unsupervised and knowledge based methods. They have used a context Matching algorithm that measures the similarity between the contexts of use corresponding to the glosses of the target word and the original sentence [6].…”

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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“…WSD is one of the most difficult problems in NLP that negatively affects the accuracy of information retrieval systems due to the sparse nature of the queries involved. It exerts a similar influence upon machine translation [28]. It is ubiquitous across all languages but it has greater challenges in the Arabic language [29] primarily due to these two issues:…”

Section: Word Sense Disambiguationmentioning

confidence: 99%

Gamification for Arabic Natural Language Processing: Ideas into Practice

Zeroual

Kah

Lakhouaja

2017

TMLAI

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Gamification is a novel IT development that focuses on how to address the use of games and apply game design techniques in a nongaming context. Recently, it has significantly attracted several researchers' attention concerned with different fields such as education, business, and information retrieval, among other fields. Most Natural Language Processing (NLP) applications use large accurately labelled data sets to achieve good performance, but these data sets are hard to obtain. The standard method to produce labelled data has been through manual labour that demands a strong engagement and a heavy financial participation. For these reasons, gamification may offer smooth possibilities for wide improvements in this field. However, gamification by itself may not yield the widelyexpected results if the incentives are not strong motivators. Further, based on a previous experience, we strongly believe that even financial incentives are not enough for the participants to increase their contributions. Therefore, we could incentivise the participants, through gamification, and through their need for language acquisition. Thus, languagebased games are adopted in this project as a direct incentive and a stronger engagement to ensure the benefits for both NLP practitioners and the language learners. In this paper, we present the first Arabic project that targets all the means to promote and explore the possibilities for research and practical applications of using games and gamification for Arabic NLP. Here, we shed light over the advantages of using a gamified approach in corpus annotation, named entity recognition, and word sense disambiguation.

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A Hybrid Approach for Arabic Word Sense Disambiguation

Cited by 22 publications

References 2 publications

Arabic Gloss WSD Using BERT

Arabic Gloss WSD Using BERT

A Framework for the Construction of a Historical Dictionary for Arabic

Gamification for Arabic Natural Language Processing: Ideas into Practice

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