Abstract
In this paper, we utilize MeSH vocabulary to capture concepts of each word appearing in questions and documents and two new methods, contextual concept smoothing language model (CCSLM) and contextual concept language model (CCLM), are proposed to find the answer sentences from biomedical literature to questions proposed by biomedical experts. The concepts employed in the models, instead of keywords, guarantee the high recall. And the contexts of each underlying answer sentence boost the precision of answers to each question. We evaluate both methods on the data collection of TREC Genomics Track 2006. The results indicate our methods are much better than the straightforward method mentioned above. Comparing to the results of Genomics Track 2006, our methods achieve about 10% higher MAP than the mean level of Genomics Track 2006.
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