Recognizing Textual Entailment Using Multiple Features and Filters
نویسندگان
چکیده
Textual entailment among sentences is an important part of applied semantic inference. In this paper we propose a novel technique to address the recognizing textual entailment challenge, which based on the distribution hypothesis that words that tend to occur in the same contexts tend to have similar meanings. Using the IDF of the overlapping words between the two propositions, we calculate the similarity between the two given propositions to infer the likelihood of entailment and then filter the results inferred. We evaluate our model on NTCIR-11 RITE dataset and then show how a combination of multiple features and filters can significantly improve the performance of recognizing textual entailmentover the best performers in those years. Our approach advances state-of-the-art Simplified Chinese NTCIR-11 RITE.
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