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Recognizing Textual Entailment Using Lexical Similarity

Authors: Jijkoun, V.; de Rijke, M.;

Recognizing Textual Entailment Using Lexical Similarity

Abstract

We describe our participation in the PASCAL-2005 Recognizing Textual EntailmentChallenge. Our method 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 differentlexical similarity measures. Our best run shows 0.55 accuracy on the test data,although the difference between our two runs is not significant. We found remarkablydifferent optimal threshold values for the development and test data. We argue that, in addition to accuracy, precision and recall are valuable measures to consider for textual entailment

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average