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الرئيسية > Learning Explicit And Implicit...>الوصف

Learning Explicit And Implicit Arabic Discourse Relations

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Keskes, Iskandar (Author)
مؤلفين آخرين: Zitoune, Farah Benamara (Co-Author) , Belguith, Lamia Hadrich (Co-Author)
المجلد/العدد: مج26, ع4
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2014
الصفحات: 398 - 416
DOI: 10.33948/0584-026-004-006
ISSN: 1319-1578
رقم MD: 973357
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
اللسانيات | اللغة العربية | علم البلاغة | الخطابة
كلمات المؤلف المفتاحية:
Discourse Relations | Segmented Discourse Repre Sentation Theory | Arabic Language
رابط المحتوى:
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المستخلص: We propose in this paper a supervised learning approach to identify discourse relations in Arabic texts. To our knowledge, this work represents the first attempt to focus on both explicit and implicit relations that link adjacent as well as non-adjacent Elementary Discourse Units (EDUs) within the Segmented Discourse Representation Theory (SDRT). We use the Discourse Arabic Treebank corpus (D-ATB) which is composed of newspaper documents extracted from the syntactically annotated Arabic Treebank v3.2 part3 where each document is associated with complete discourse graph according to the cognitive principles of SDRT. Our list of discourse relations is composed of a three-level hierarchy of 24 relations grouped into 4 top-level classes. To automatically learn them, we use state of the art features whose efficiency has been empirically proved. We investigate how each feature contributes to the learning process. We report our experiments on identifying fine grained discourse relations, mid-level classes and also top-level classes. We compare our approach with three baselines that are based on the most frequent relation, discourse connectives and the features used by Al-Saif and Markert (2011). Our results are very encouraging and outperform all the baselines with an F-score of 78.1% and an accuracy of 80.6%.
ISSN: 1319-1578

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