Dragomir R. Radev
ABSTRACT
In this paper, we describe a system to rank suspected answers to natural language questions. We process both corpus and query using a new technique, predictive annotation, which augments phrases in texts with labels anticipating their being targets of certain kinds of questions. Given a natural language question, our IR system returns a set of matching passages, which we then rank using a linear function of seven predictor variables. We provide an evaluation of the techniques based on results from the TREC Q&A evaluation in which our system participated.
- Eric Breck, John Burger, David House, Marc Light, and Inderjeet Mani. 1999. Question answering from large document collections. In Proceedings of AAAI Fall Symposium on Question Answering Systems, North Falmouth, Massachusetts.Google Scholar
- Roy Byrd and Yael Ravin. 1998. Identifying and extracting relations in text. In Proceedings of NLDB, Klagenfurt, Austria.Google Scholar
- Sanda Harabagiu and Steven J. Maiorano. 1999. Finding answers in large collections of texts: Paragraph indexing + abductive inference. In Proceedings of AAAI Fall Symposium on Question Answering Systems, North Falmouth, Massachusetts.Google Scholar
- Vladimir Kulyukin, Kristian Hammond, and Robin Burke. 1998. Answering questions for an organization online. In Proceedings of AAAI, Madison, Wisconsin. Google ScholarDigital Library
- Julian M. Kupiec. 1993. MURAX: A robust linguistic approach for question answering using an on-line encyclopedia. In Proceedings, 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- John Prager, Dragomir R. Radev, Eric Brown, Anni Coden, and Valerie Samn. 1999. The use of predictive annotation for question answering in TREC8. In Proceedings of TREC8, Gaithersburg, Maryland.Google Scholar
- Dragomir R. Radev and Kathleen R. McKeown. 1997. Building a generation knowledge source using internet-accessible newswire. In Proceedings of the 5th Conference on Applied Natural Language Processing, pages 221--228, Washington, DC, April. Google ScholarDigital Library
- Ellen Voorhees. 1994. Query expansion using lexical-semantic relations. In Proceedings of ACM SIGIR, Dublin, Ireland. Google ScholarDigital Library
- Nina Wacholder, Yael Ravin, and Misook Choi. 1997. Disambiguation of proper names in text. In Proceedings of the Fifth Applied Natural Language Processing Conference, Washington, D.C. Association for Computational Linguistics. Google ScholarDigital Library
- Ranking suspected answers to natural language questions using predictive annotation
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