Work

Correction Annotation for Non-Native Arabic Texts: Guidelines and Corpus

Year: 2015

Type: erratum

Abstract: We present our correction annotation guidelines to create a manually corrected nonnative (L2) Arabic corpus.We develop our approach by extending an L1 large-scale Arabic corpus and its manual corrections, to include manually corrected non-native Arabic learner essays.Our overarching goal is to use the annotated corpus to develop components for automatic detection and correction of language errors that can be used to help Standard Arabic learners (native and non-native) improve the quality of the Arabic text they produce.The created corpus of L2 text manual corrections is the largest to date.We evaluate our guidelines using inter-annotator agreement and show a high degree of consistency.

Source: Linguistic Annotation Workshop

DOI: 10.3115/v1/w15-1614

Authors: Wajdi Zaghouani, Nizar Habash, Houda Bouamor, Alla Rozovskaya, Behrang Mohit, Abeer Heider, Kemal Oflazer

Institutions: Carnegie Mellon University Qatar, Carnegie Mellon University, New York University Abu Dhabi, Columbia University, Qatar University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2251945664

Cites: 30

Cited by: 29

Related to: 10