The Choice of Plausible Alternatives (COPA) task in SemEval-2012 presents a series of forced-choice questions wherein each question provides a premise and two viable cause or effect scenarios. The correct answer is the cause or effect that is the most plausible. This paper describes the COPACETIC system developed by the University of Texas at Dallas (UTD) for this task. We approach this task by casting it as a classification problem and using features derived from bigram co-occurrences, TimeML temporal links between events, single-word polarities from the Harvard General Inquirer, and causal syntactic dependency structures within the gigaword corpus. Additionally, we show that although each of these components improves our score for this evaluation, the difference in accuracy between using all of these features and using bigram co-occurrence information alone is not statistically significant.
['Travis Goodwin', 'Bryan Rink', 'Kirk Roberts', 'Sanda M. Harabagiu']
UTDHLT: COPACETIC System for Choosing Plausible Alternatives