tailieunhanh - Báo cáo khoa học: "Question Answering using Constraint Satisfaction: QA-by-Dossier-with-Constraints"

QA-by-Dossier-with-Constraints is a new approach to Question Answering whereby candidate answers’ confidences are adjusted by asking auxiliary questions whose answers constrain the original answers. These constraints emerge naturally from the domain of interest, and enable application of real-world knowledge to QA. We show that our approach significantly improves system performance (75% relative improvement in F-measure on select question types) and can create a “dossier” of information about the subject matter in the original question. . | Question Answering using Constraint Satisfaction QA-by-Dossier-with-Constraints John Prager . Watson Research Ctr. Yorktown Heights . 10598 jprager@ Jennifer Chu-Carroll . Watson Research Ctr. Yorktown Heights . 10598 jencc@ Krzysztof Czuba . Watson Research Ctr. Yorktown Heights . 10598 kczuba@ Abstract QA-by-Dossier-with-Constraints is a new approach to Question Answering whereby candidate answers confidences are adjusted by asking auxiliary questions whose answers constrain the original answers. These constraints emerge naturally from the domain of interest and enable application of real-world knowledge to QA. We show that our approach significantly improves system performance 75 relative improvement in F-measure on select question types and can create a dossier of information about the subject matter in the original question. 1 Introduction Traditionally Question Answering QA has drawn on the fields of Information Retrieval Natural Language Processing NLP Ontologies Data Bases and Logical Inference although it is at heart a problem of NLP. These fields have been used to supply the technology with which QA components have been built. We present here a new methodology which attempts to use QA holistically along with constraint satisfaction to better answer questions without requiring any advances in the underlying fields. Because NLP is still very much an error-prone process QA systems make many mistakes accordingly a variety of methods have been developed to boost the accuracy of their answers. Such methods include redundancy getting the same answer from multiple documents sources or algorithms deep parsing of questions and texts hence improving the accuracy of confidence measures inferencing proving the answer from information in texts plus background knowledge and sanity-checking veri fying that answers are consistent with known facts . To our knowledge however no QA system deliberately asks additional questions