Learning Causality for Arabic - Proclitics

Conference paper

Sadek, J. and Meziane, F. 2018. Learning Causality for Arabic - Proclitics. 4th Arabic Computational Linguistics, ACLing 2018. Dubai, UAE 17 - 19 Nov 2018 Elsevier. https://doi.org/10.1016/j.procs.2018.10.469
AuthorsSadek, J. and Meziane, F.
TypeConference paper
Abstract

The use of prefixed particles is a prevalent linguistic form to express causation in Arabic Language. However, such particles are complicated and highly ambiguous as they imply different meanings according to their position in the text. This ambiguity emphasizes the high demand for a large-scale annotated corpus that contains instances of these particles. In this paper, we present the process of building our corpus, which includes a collection of annotated sentences each containing an instance of a candidate causal particle. We use the corpus to construct and optimize predictive models for the task of causation recognition. The performance of the best models is significantly better than the baselines. Arabic is a less–resourced language and we hope this work would help in building better Information Extraction systems.

KeywordsArabic Annotated Corpus; Arabic Causality Extraction; Causal Relations; Discourse Relation Recognition
Year2018
Conference4th Arabic Computational Linguistics, ACLing 2018
JournalProcedia Computer Science
PublisherElsevier
ISSN18770509
Digital Object Identifier (DOI)https://doi.org/10.1016/j.procs.2018.10.469
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85065707885&partnerID=MN8TOARS
Journal citationVol 142 (2018), pp. 141 - 149
Web address (URL) of conference proceedingshttps://www.proceedings.com/42166.html
Output statusPublished
Publication dates
Online15 Dec 2018
Publication process dates
Deposited05 Jun 2023
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