Bootstrapping a WordNet for an Arabic dialect from other WordNets and dictionary resources

Cavalli-Sforza, Violetta; Saddiki, Hind; Bouzoubaa, Karim; Abouenour, Lahsen; Maamouri, Mohamed; Goshey, Emily

doi:10.1109/aiccsa.2013.6616508

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…International Corpus of Arabic (ICA) [21] is fundamentally used to handle Arabic morphological pluralism. AWN [19] [20] is exploited to extract semantic relations for the lexical units. A system for automatic ED and mood recognition was built using a combination of ML and rule-based methods.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…There remains, however, the semantic problem that primarily emanating from the lack of Arabic semantic resources. A few types of research concerning ATC used Arabic Word Net (AWN) for improving classification results such as [19] [20]. Here, we expand AWN as a semantic resource for synonyms substitution and root extraction.…”

Section: B Arabic Opinion Classificationmentioning

confidence: 99%

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Customer Opinions Evaluation: A Case Study on Arabic Tweets

Ali¹

2019

IJAIA

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This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural Language Processing (NLP) with different types of analyses had performed. Second, we present an automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate. Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: B Arabic Opinion Classificationmentioning

confidence: 99%

Customer Opinions Evaluation: A Case Study on Arabic Tweets

Ali¹

2019

IJAIA

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“…Boujelbane, BenAyed, and Belguith (), built a Tunisian dialectal corpus in order to create a language model for a speech recognition system for a Tunisian Broadcast News company. Cavalli‐Sforza et al () created an Iraqi Arabic WordNet using an English‐Iraqi dictionary and the modern standard Arabic version of WordNet as well as the English WordNet. Moreover, a Tunisian dialect WordNet was built in Bouchlaghem, Elkhlifi, and Faiz () starting from a Tunisian corpus.…”

Section: Native Language Language Varieties and Dialects Identificamentioning

confidence: 99%

A survey on author profiling, deception, and irony detection for the Arabic language

Rosso

Rangel

Farías

et al. 2018

Language and Linguist. Compass

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The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.

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“…In (Cavalli-Sforza et al, 2013) an Iraqi Word-Net is presented based on the MSA WordNet, the English WordNet, and an English-Iraqi dictionary. A Tunisian dialect WordNet was built in (Bouchlaghem & Elkhlifi, 2014) starting from a Tunisian corpus.…”

Section: Building Lexicons and Lexical Analysismentioning

confidence: 99%