The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Textual entailment recognition based on dependency analysis and wordnet
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Formal online errors/bugs documentation of real-time software is a difficult and error prone task. Conceptual and tool support for this activity plays a central role in the agenda of building large complex software products, especially if this software targets market abroad that requires a continuous massive inflow of data customers' needs and a regarding product requirements. Unfortunately, the manual linkage that is routinely performed today is cumbersome, time-consuming, and error-prone. This paper presents a framework to explore the redundancy of error reports in online documentation. The framework employs a recent natural language processing technique called Textual Entailment.