Word2vec for Arabic Word Sense Disambiguation

Laatar, Rim; Aloulou, Chafik; Belghuith, Lamia Hadrich

doi:10.1007/978-3-319-91947-8_32

Cited by 11 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These small signs added to letters help readers differentiate between similar words. Nonetheless, omitting diacritics from some words can result in numerous lexical ambiguities (Laatar et al, 2018). Lastly, Arabic is highly inflectional, with a very complex morphology.…”

Section: The Arabic Languagementioning

confidence: 99%

ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence

Alsarra,

Abdeljaber,

Yang

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

View full text Add to dashboard Cite

This study investigates the use of Natural Language Processing (NLP) methods to analyze politics, conflicts and violence in the Middle East using domain-specific pre-trained language models. We introduce Arabic text and present ConfliBERT-Arabic, a pre-trained language models that can efficiently analyze political, conflict and violence-related texts. Our technique hones a pre-trained model using a corpus of Arabic texts about regional politics and conflicts. Performance of our models is compared to baseline BERT models. Our findings show that the performance of NLP models for Middle Eastern politics and conflict analysis are enhanced by the use of domain-specific pre-trained local language models. This study offers political and conflict analysts, including policymakers, scholars, and practitioners new approaches and tools for deciphering the intricate dynamics of local politics and conflicts directly in Arabic.

show abstract

Section: The Arabic Languagementioning

confidence: 99%

ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence

Alsarra,

Abdeljaber,

Yang

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

View full text Add to dashboard Cite

show abstract

“…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. The authors of [23] employed word2vec [15] to generate a vector representation for the target word's context sentence and the gloss sentence. Then, cosine similarity was used to match the gloss vector and the context vector.…”

Section: Related Workmentioning

confidence: 99%

“…Table 9 also shows the original paper results of other models according to their test set. We performed McNemar's significance test [32] between Model II and Laatar [25] and between Model II and Laatar [23]; we find that Model II is significantly better than Laatar [25] and Laatar [23] and that the p-value is less than 0.01. Bekkali [24] 1217 text --85%…”

mentioning

confidence: 91%

“…Table 9 compares our proposed models against other embedding based models that depend on representing gloss and the context sentences in the vector space such as in our model. We compare our proposed models with two models, Laatar [25] and Laatar [23] which are the most recent Arabic WSD works. The test data of the two models are not available; thus, in order to make a fair comparison, we redeveloped their models and tested them on our benchmark.…”

mentioning

confidence: 99%

“…Laatar [25] represents each sentence by the mean of its words vectors. The authors in [23] used Word2Vec [15] language model to represent the words of the sentence in the vector space and then they represented the sentence by adding its words vector representation. After calculating the sentences' vectors, both models measure the cosine similarity between the context sentence vector and the gloss sentences vector and choose the gloss with the highest similarity score.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Arabic Gloss WSD Using BERT

2021

View full text Add to dashboard Cite

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.

show abstract

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

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.

show abstract

Word2vec for Arabic Word Sense Disambiguation

Cited by 11 publications

References 0 publications

ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence

ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence

Arabic Gloss WSD Using BERT

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

Contact Info

Product

Resources

About