A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Extraction of coherent relevant passages using hidden Markov models
ACM Transactions on Information Systems (TOIS)
Using Clustering Approaches to Open-Domain Question Answering
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
PTM: probabilistic topic mapping model for mining parallel document collections
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Extraction of contextual information from medical case research report using WordNet
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
| Hi-index | 0.01 |
Answer patterns have been shown to improve the perfor-mance of open-domain factoid QA systems. Their use, however, requires either constructing the patterns manually or developing algorithms for learning them automatically. We present here a simpler approach that extends the techniques of language modeling to create answer models. These are language models trained on the correct answers to training questions. We show how they fit naturally into a probabilis-tic model for answer passage retrieval and demonstrate their effectiveness on the TREC 2002 QA Corpus.