Learning causality for Arabic - proclitics

Sadek, J and Meziane, F ORCID: https://orcid.org/0000-0001-9811-6914 2018, 'Learning causality for Arabic - proclitics' , Procedia Computer Science, 142 , pp. 141-149.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Procedia Computer Science
Publisher: Elsevier
ISSN: 1877-0509
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 02 Nov 2018 10:24
Last Modified: 14 Nov 2019 15:00
URI: http://usir.salford.ac.uk/id/eprint/48832

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