Alfaifi, AYG, Atwell, E and Abuhakema, G (2013) Error Annotation of the Arabic Learner Corpus: A New Error Tagset. In: Language Processing and Knowledge in the Web. 25th International Conference, GSCL 2013, 25-27 Sep 2013, Darmstadt, Germany. Springer , 14 - 22 (9). ISBN 978-3-642-40722-2
Abstract
This paper introduces a new two-level error tagset, AALETA (Alfaifi Atwell Leeds Error Tagset for Arabic), to be used for annotating the Arabic Learner Corpora (ALC). The new tagset includes six broad classes, subdivided into 37 more specific error types or subcategories. It is easily understood by Arabic corpus error annotators. AALEETA is based on an existing error tagset for Arabic corpora, ARIDA, created by Abuhakema et al. [1], and a number of other error-analysis studies. It was used to annotate texts of the Arabic Learner Corpus [2]. The paper shows the tagset broad classes and types or subcategories and an example of annotation. The understandability of AALETA was measured against that of ARIDA, and the preliminary results showed that AALETA achieved a slightly higher score. Annotators reported that they preferred using AALETA over ARIDA.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013, Springer. This is an author produced version of a paper published in Language Processing and Knowledge in the Web (Lecture note in computer science). Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at link.springer.com |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Dec 2013 10:51 |
Last Modified: | 19 Dec 2022 13:25 |
Published Version: | http://dx.doi.org/10.1007/978-3-642-40722-2_2 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-642-40722-2_2 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:77161 |