Semi-automatic Domain Ontology Construction from Spoken Corpus in Tunisian Dialect: Railway Request Information

Karoui, Jihen; Graja, Marwa; Boudabous, Mohamed Mahdi; Belguith, Lamia Hadrich

doi:10.3991/ijes.v1i1.2925

Cited by 4 publications

(3 citation statements)

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“…Other researchers worked on domain ontologies for the TD processing in dialogue systems (Graja et al, 2011a(Graja et al, , 2011bKaroui et al, 2013aKaroui et al, , 2013bGraja et al, 2015). Graja et al (2011aGraja et al ( , 2011b) used ontologies to cover the lexicon used in train stations.…”

Section: Related Workmentioning

confidence: 99%

“…They used the TERMINAE methodology (Biébow and Szulman, 1999) and resorted to the TuDiCoI corpus built by Graja et al (2010). As for Karoui et al (2013aKaroui et al ( , 2013b, they proposed a hybrid method, combining a statistical approach for the extraction of terms and concepts and a linguistic approach for the extraction of semantic relations, for the semi-automatic construction of a domain ontology. The latter was called railway information ontology (RIO), and was constructed from the spoken TD corpus TuDiCoI (Graja et al, 2010), in order to semantically label TD statements.…”

Section: Related Workmentioning

confidence: 99%

“…We should however note that the majority of these resources still have limits in terms of size, content and availability. Some of the built corpora have indeed, a limited coverage to a specific domain, such as the TuDiCoI corpus (Graja et al, 2010) and the ontologies of Karoui et al (2013aKaroui et al ( , 2013b that are limited to the railway station interactions between clients and staff and, therefore do not cover a broad vocabulary. TD corpora constructed by Graja et al (2010), Zribi et al (2013) and Masmoudi et al (2014c) were based on a speech transcription.…”

Section: Related Workmentioning

confidence: 99%

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Contributions to the automatic processing of the user-generated Tunisian dialect on the social web

et al. 2020

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With the growing use of social media in the Arab world, Arabic dialects are rapidly spreading on the web, leading to a growing interest from NLP researchers. These dialects are however, still under-resourced languages which is a major obstacle to their study and processing. In this paper, we focus on the automatic processing of the user-generated Tunisian dialect (TD) on the social web and propose an approach that aids to automatically generate TD language resources. This approach exploits the large amounts of textual productions on the social web to extract and generate dialectal content. It is based on two main NLP components, namely the TD identification and the TD transliteration. A machine learning approach using conditional random fields is proposed for implementing these two components and reached an accuracy of 87.45 for the TD identification and 90.49 for the automatic generation of dialectal contents by transliteration.

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