Word Embedding for Arabic Word Sense Disambiguation to create a Historical Dictionary for Arabic Language

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

doi:10.1109/csit.2018.8486159

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Alian and Awajan (2020) represented the effect of word embeddings on constructing an Arabic sense inventory, taking advantage of a word sense induction algorithm. In Laatar et al (2018a), embeddings are also used in solving AWSD; the authors showed how word embeddings could be used by analyzing different parameters. The work in Laatar et al (2018b) used word2vec and showed how word embeddings could be used in Arabic WSD tasks.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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show abstract

“…Word embeddings in Arabic have also been suggested as a way to solve the problem of word disambiguation, a frequent task in natural language processing. Specifically, Laatar et al [14] suggested using this approach to create a dictionary that illustrates the development of the meaning and usage of Arabic words, which would help to preserve the Arabic cultural heritage. The word embedding generation technique involved is the Word2vec architecture.…”

Section: Related Work a Word Embedding Techniquesmentioning

confidence: 99%

Improvement and Optimization of Vulnerability Detection Methods for Ethernet Smart Contracts

Yang

Zhu

2023

IEEE Access

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Smart contracts based on blockchain are widely used in finance, management, Internet of Things, healthcare, and other fields. However, with the rapid development of smart contracts, the corresponding security vulnerability attack cases occur frequently. Existing Ethereum smart contract vulnerability detection tools based on static analysis techniques rely too much on expert rules, for this reason, this paper proposes an Ethereum smart contract vulnerability detection method SCSVM based on support vector machine technology. A representation of smart contracts is constructed based on the word-to-vector technique, the features of Ethereum smart contracts are extracted based on the support vector machine technique, and these features are combined to identify vulnerabilities. Experiments on Smartbugs and Smartbugs-wild show that SCSVM is significantly effective. It achieves a detection accuracy of 87.51%, outperforming five typical static analysis vulnerability detection tools in terms of F1-score. To alleviate the problems of deep learning methods over-relying on large-scale data to train models and collecting a large number of smart contract attack samples in a short period, this paper proposes a basic learner-meta-learner framework, SCLMF. solc-based acquisition of the bytecode of Ethereum smart contract Solidity, on which smart contract representations are constructed via Python and the use of SCLMF for vulnerability detection. The experiments on WScrawlD show that SCLMF has a certain detection effect. Also, to further verify the effectiveness of SCLMF, experiments were conducted on Omniglot, and the detection accuracy was 96.7% and 98.5% under 5-way 1-shot and 5-way 5-shot conditions, respectively, which exceeded Memory-Augmented Neural Networks and CONVOLUTIONAL SIAMESE NETS. In summary, the experiments proved the effectiveness of SCSVM and SCLMF in Ethereum smart contract vulnerability detection.

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EnhancedBERT: A feature-rich ensemble model for Arabic word sense disambiguation with statistical analysis and optimized data collection

Kaddoura,

Nassar

2024

Journal of King Saud University - Computer and Information Scie

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Word Embedding for Arabic Word Sense Disambiguation to create a Historical Dictionary for Arabic Language

Cited by 5 publications

References 8 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

Improvement and Optimization of Vulnerability Detection Methods for Ethernet Smart Contracts

EnhancedBERT: A feature-rich ensemble model for Arabic word sense disambiguation with statistical analysis and optimized data collection

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