Abstract
We describe our submission to the PASCAL Recognizing Textual Entailment Challenge, which attempts to isolate the set of Text-Hypothesis pairs whose categorization can be accurately predicted based solely on syntactic cues. Two human annotators examined each pair, showing that a surprisingly large proportion of the data – 34% of the test items – can be handled with syntax alone, while adding information from a general-purpose thesaurus increases this to 48%.
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© 2006 Springer-Verlag Berlin Heidelberg
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Vanderwende, L., Dolan, W.B. (2006). What Syntax Can Contribute in the Entailment Task. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_11
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DOI: https://doi.org/10.1007/11736790_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
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