COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Knowledge Representation and the Semantics of Natural Language (Cognitive Technologies)
Knowledge Representation and the Semantics of Natural Language (Cognitive Technologies)
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The paper presents an approach to multi-stream question answering (QA) using deep semantic parsing and logical validation for filtering answer candidates. A robust entailment check is accomplished by embedding the prover in a relaxation loop. Fallback strategies ensure a graceful degradation of performance in the case of parsing problems. The logical validity score is complemented by false-positive tests and heuristic quality indicators which also affect the selection of the most trusted answers. Separate criteria are used for choosing a suitable 'witness', i.e. a text passage which substantiates the answer. We present two experiments in which the method is applied for merging the results of various state-of-the-art QA systems. The evaluation demonstrates that the approach is applicable to heterogeneous QA streams -- in particular it improves results for a combination of precision-oriented and recall-oriented answer streams. The method automatically adapts to these characteristics of a QA system by learning parameters from a training sample.