Year: 2012
Type: conference-paper
Abstract: We train and test two probabilistic taggers for Arabic phrase break prediction on a purpose-built, “gold standard”, boundary-annotated and PoS-tagged Qur‟an corpus of 77430 words and 8230 sentences. In a related LREC paper (Brierley et al., 2012), we cover dataset build. Here we report on comparative experiments with off-the-shelf N-gram and HMM taggers and coarse-grained feature sets for syntax and prosody, where the task is to predict boundary locations in an unseen test set stripped of boundary annotations by classifying words as breaks or non-breaks. The preponderance of non-breaks in the training data sets a challenging baseline success rate: 85.56%. However, we achieve significant gains in accuracy with the trigram tagger, and significant gains in performance recognition of minority class instances with both taggers via Balanced Classification Rate. This is initial work on a long-term research project to produce annotation schemes, language resources, algorithms, and applications for Classical and Modern Standard Arabic.
Authors: Majdi Sawalha, Claire Brierley, Eric Atwell
Institutions: University of Jordan, University of Leeds
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W20589053