Learning Causality for Arabic - Proclitics
Conference paper
Authors | Sadek, J. and Meziane, F. |
---|---|
Type | Conference 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. |
Keywords | Arabic Annotated Corpus; Arabic Causality Extraction; Causal Relations; Discourse Relation Recognition |
Year | 2018 |
Conference | 4th Arabic Computational Linguistics, ACLing 2018 |
Journal | Procedia Computer Science |
Publisher | Elsevier |
ISSN | 18770509 |
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 citation | Vol 142 (2018), pp. 141 - 149 |
Web address (URL) of conference proceedings | https://www.proceedings.com/42166.html |
Output status | Published |
Publication dates | |
Online | 15 Dec 2018 |
Publication process dates | |
Deposited | 05 Jun 2023 |
https://repository.derby.ac.uk/item/9z12q/learning-causality-for-arabic-proclitics
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