- Abstract:
-
In question answering, two main kinds of matching methods for finding answer sentences for a question are term-based approaches which are simple, efficient, effective, and yield high recall and event-based approaches that take syntactic and semantic information into account. The latter often sacrifice recall for increased precision, but actually capture the meaning of the events denoted by the textual units of a passage or sentence. We propose a robust, data-driven method that learns the mapping between questions and answers using logistic regression and show that combining term-based and event-based approaches significantly outperforms the individual methods.
- Copyright:
- 2007 by Saarland University (Spoken Language Systems) and the University of Edinburgh. All Rights Reserved
- Links To Paper
- local copy
- Bibtex format
- @InProceedings{EDI-INF-RR-1046,
- author = {
Michael Wiegand
and Jochen Leidner
and Dietrich Klakow
},
- title = {Combining Term-Based and Event-Based Matching for Question Answering},
- book title = {Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
- publisher = {Association for Computing Machinery},
- year = 2007,
- month = {Jul},
- pages = {715-716},
- doi = {10.1145/1277741.1277873},
- url = {http://www.iccs.informatics.ed.ac.uk/~s0239229/documents/Wiegand-Leidner-Klakow-2007-SIGIR.pdf},
- }
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