Building an Arabic Linguistic Resource from a Treebank: The Case of Property Grammar

Bahloul, Raja Bensalem; Elkarwi, Marwa; Haddar, Kais; Blache, Philippe

doi:10.1007/978-3-319-10816-2_30

Cited by 6 publications

(2 citation statements)

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“…Its grammar is adapted to the MSA, the input data we are studying. In addition, only the ATB offers a constituency-based representation for its syntactic structures [Bensalem 2014; Al-Ghamdi 2021; Habash 2022]. This representation is adequate with the Property Grammar formalism.…”

Section: Learning Corpus: the Atb Treebankmentioning

confidence: 99%

An Arabic Probabilistic Parser Based on a Property Grammar

Bensalem,

Haddar,

Blache

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The specificities of the Arabic parsing such as the agglutination, the vocalization and the relatively order-free of words in the Arabic sentences, remain a major issue to consider. To promote its robustness, such parser should define different types of constraints. The Property Grammar formalism (PG) verify the satisfiability of the constraints directly on the units of the structure, thanks to its properties (or relations). In this context, we propose to build a probabilistic parser with syntactic properties, using a PG, and we measure the production rules in terms of different implicit information and in particular the syntactic properties. We experimented our parser on the treebank ATB using the parsing algorithm CYK and obtained encouraging results. Our method is also automatic for the implementation of most property type. Its generalization for other languages or corpus domains (using treebanks) could be a good perspective. Its combination with the pre-trained models of BERT may also make our parser faster.

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Section: Learning Corpus: the Atb Treebankmentioning

confidence: 99%

An Arabic Probabilistic Parser Based on a Property Grammar

Bensalem,

Haddar,

Blache

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

Self Cite

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“…Annotated Corpora: (Atserias et al, 2010;Refaee & Rieser, 2014;Caseli et al, 2009;Iida & Tokunaga, 2014;Oostdijk, 1999;Hangyo et al, 2012;Liu, Loh, & Tor, 2004;AbdelRaouf, et al 2010;Samy et al 2006;Megyesi et al, 2006;Roberts, 2009;Bahloul et al, 2014;Riesa et al, 2006;Baroni and Ueyama, 2006;Baobao, 2004;Aleahmad et al, 2009;Dukes & Habash, 2010;Rytting et al, 2014;El-Haj & Koulali, 2013; Rushdi-Saleh et al, 2011).…”

Section: Corpora Annotationunclassified

Sentiment analysis and resources for informal Arabic text on social media

Itani¹

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Online content posted by Arab users on social networks does not generally abide by the grammatical and spelling rules. These posts, or comments, are valuable because they contain users' opinions towards different objects such as products, policies, institutions, and people. These opinions constitute important material for commercial and governmental institutions. Commercial institutions can use these opinions to steer marketing campaigns, optimize their products and know the weaknesses and/ or strengths of their products. Governmental institutions can benefit from the social networks posts to detect public opinion before or after legislating a new policy or law and to learn about the main issues that concern citizens. However, the huge size of online data and its noisy nature can hinder manual extraction and classification of opinions present in online comments. Given the irregularity of dialectal Arabic (or informal Arabic), tools developed for formally correct Arabic are of limited use. This is specifically the case when employed in sentiment analysis (SA) where the target of the analysis is social media content. This research implemented a system that addresses this challenge. This work can be roughly divided into three blocks: building a corpus for SA and manually tagging it to check the performance of the constructed lexicon-based (LB) classifier; building a sentiment lexicon that consists of three different sets of patterns (negative, positive, and spam); and finally implementing a classifier that employs the lexicon to classify Facebook comments. In addition to providing resources for dialectal Arabic SA and classifying Facebook comments, this work categorises reasons behind incorrect classification, provides preliminary solutions for some of them with focus on negation, and uses regular expressions to detect the presence of lexemes. This work also illustrates how the constructed classifier works along with its different levels of reporting. Moreover, it compares the performance of the LB classifier against Naïve Bayes classifier and addresses how NLP tools such as POS tagging and Named Entity Recognition can be employed in SA. In addition, the work studies the performance of the implemented LB classifier and the developed sentiment lexicon when used to classify other corpora used in the literature, and the performance of lexicons used in the literature to classify the corpora constructed in this research. With minor changes, the classifier can be used in domain classification of documents (sports, science, news, etc.). The work ends with a discussion of research questions arising from the research reported.

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A Property Grammar-Based Method to Enrich the Arabic Treebank ATB

Bahloul¹,

Haddar²,

Blache³

2016

Communications in Computer and Information Science

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Abstract. We present a method based on the formalism of Property Grammars to enrich the Arabic treebank ATB with syntactic constraints (so-called properties). The Property Grammar formalism is an effectively constraint-based approach that directly specifies the constraints on information categories. This can facilitate the enrichment process. The latter is based on three phases: the problem formalization, the Property Grammar induction from the ATB and the treebank regeneration with a new syntactic property-based representation. The enrichment of the ATB can make it more useful for many NLP applications such as the ambiguity resolution. This allows also the acquisition of new linguistic resources and the ease of the probabilistic parsing process. This enrichment process is purely automatic and independent from any language and source corpus formalism. This motivates its reuse. We obtained good and encouraging experiment results and various properties of different types.

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Building an Arabic Linguistic Resource from a Treebank: The Case of Property Grammar

Cited by 6 publications

References 1 publication

An Arabic Probabilistic Parser Based on a Property Grammar

An Arabic Probabilistic Parser Based on a Property Grammar

Sentiment analysis and resources for informal Arabic text on social media

A Property Grammar-Based Method to Enrich the Arabic Treebank ATB

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