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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Recognizing Textual Entailment: Is Word Similarity Enough?
verfasst von : Valentin Jijkoun, Maarten de Rijke
Erschienen in: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment
Verlag: Springer Berlin Heidelberg
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We describe the system we used at the PASCAL-2005 Recognizing Textual Entailment Challenge. Our method for recognizing entailment is based on calculating “directed” sentence similarity: checking the directed “semantic” word overlap between the text and the hypothesis. We use frequency-based term weighting in combination with two different word similarity measures.
Although one version of the system shows significant improvement over randomly guessing decisions (with an accuracy score of 57.3), we show that this is only due to a subset of the data that can be equally well handled by simple word overlap. Furthermore, we give an in-depth analysis of the system and the data of the challenge.