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.
|
PDF
- Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (536kB) | Preview |
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 |
Actions (login required)
 |
Edit record (repository staff only) |
Tools
Tools