SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
Probabilistic models of indexing and searching
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
Pronunciation modeling for improved spelling correction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Linear discriminant model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ProbFuse: a probabilistic approach to data fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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In this paper we propose a way to cope with questions typed by dyslexic users as they are usually a deformation of the intended query that cannot be corrected with classical spellcheckers. We first propose a new model for statistic question answering systems based on a probabilistic information retrieval model and a combination of results. This model allows a multiple weighted terms query as an input. We also introduce a phonology based approach at the sentence level to derive possible intended terms from typed questions. This approach uses the finite state machine framework to go from phonetic hypothesis and spellchecker proposals to hypothesized sentences thanks to a language model. The final weighted queries are obtained thanks to posterior probabilities computation. They are evaluated according to new density and appearance rating measures which adapt recall and precision to non binary data.