In this paper, we propose two weighting techniques to improve performances of query expansion in biomedical document retrieval, especially when a short biomedical term in a query is expanded with its synonymous multi-word terms. When a query contains synonymous terms of different lengths, a traditional IR model highly ranks a document containing a longer terminology because a longer terminology has more chance to be matched with a query. However, such preference is clearly inappropriate and it often yields an unsatisfactory result. To alleviate the bias weighting problem, we devise a method of normalizing the weights of query terms in a long multi-word biomedical term, and a method of discriminating terms by using inverse terminology frequency which is a novel statistics estimated in a query domain. The experiment results on MEDLINE corpus show that our two simple techniques improve the retrieval performance by adjusting the inadequate preference for long multi-word terminologies in an expanded query.

We propose a definitional question answering system that extracts phrases using syntactic patterns which are easily constructed manually and can reduce the coverage problem. Experimental results show that our phrase extraction system outperforms a sentence extraction system, especially for selecting concise answers, in terms of recall and precision, and indicate that the proper text unit of answer candidates and the final answer has a significant effect on the system performance.

The multinomial naive Bayes model has been widely used for probabilistic text classification. However, the parameter estimation for this model sometimes generates inappropriate probabilities. In this paper, we propose a topic document model for the multinomial naive Bayes text classification, where the parameters are estimated from normalized term frequencies of each training document. Experiments are conducted on Reuters 21578 and 20 Newsgroup collections, and our proposed approach obtained a significant improvement in performance compared to the traditional multinomial naive Bayes.