A genetic algorithm for learning significant phrase patterns in radiology reports
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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There are two key ingredients in the general framework of language models used in information retrieval, one is importance weighting, the other is word relationship computing. A series of improvements are made to these ingredients of the general framework of language models which is used in domain-specific information retrieval. First, an EM algorithm is proposed to estimate the importance weights of query terms, and the Bayesian smoothing is used to compute the productive probabilities of important terms. Next, a new algorithm based on Dynamic Bayesian Network is proposed for obtaining the explanation probabilities between terms. Experiment shows that the improved model performs remarkably better for domain-specific information retrieval than some traditional retrieval techniques, and the extended framework has good expansibility.