Corpus-Based Prediction of Coordination Ambiguity in Arabic
Wafaa Daffa, Raad Alshahry, Imtiaz Hussain Khan
International Journal of Computational Linguistics Research, Vol. 6, No. 3, 2015.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v6n3/v6n3_3.pdf
Abstract:
Syntactic ambiguity is a common problem in Arabic language. We are exploring the possibility of using corpus based
word collocation data to predict different interpretations of a potentially ambiguous sentence in Arabic. As a case
study, we address the problem of disambiguating coordination structures in Arabic to determine how the external modifier
(adjective) applies to the coordinated words (nouns) like Ø§Ù„Ù‚Ø·Ø· ÙˆØ§Ù„ÙƒÙ„Ø§Ø¨ Ø§Ù„Ø³ÙˆØ¯Ø§Ø¡ (black cats and dogs). In this paper, we report
on an empirical study in which participants were presented with a sequence of trials, each of which consists a potentially
ambiguous sentence followed by a comprehension question that relates to the preceding sentence. The study reveals that
lexical co-occurrence information, derived using Kilgarriffâ€™s Sketch Engine operated on a ca. 1.7 millions words Arabic Web
Corpus, can be used to predict the most likely interpretation of a potentially ambiguous sentence.