A Language Independent Approach for Recognizing Textual Entailment (original) (raw)
Textual Entailment Recognition (RTE) was proposed as a generic task, aimed at building modules capable of capturing the semantic variability of texts and performing natural language inferences. These modules can be then included in any NLP system, improving its performance in fine-grained semantic differentiation. The first part of the article describes our approach aimed at building a generic, language-independent TE system that would eventually be used as a module within a QA system. We evaluated the accuracy of this system by building two instances of it -for English and Romanian and testing them on the data from the RTE3 competition. In the second part we show how we applied the steps described in [1] and adapted this system in order to include it as module in a QA system architecture. Lastly, we show the results obtained, which point out significant growth in precision.
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