Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Automating temporal annotation with TARSQI
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
RDF-3X: a RISC-style engine for RDF
Proceedings of the VLDB Endowment
Learning semantic links from a corpus of parallel temporal and causal relations
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2012 task 7: choice of plausible alternatives: an evaluation of commonsense causal reasoning
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
SemEval-2012 task 7: choice of plausible alternatives: an evaluation of commonsense causal reasoning
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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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.