@article{M0514F3D8,
title = "Answer Snippet Retrieval for Question Answering of Medical Documents",
journal = "Journal of KIISE, JOK",
year = "2016",
issn = "2383-630X",
doi = "",
author = "Hyeon-gu Lee,Minkyoung Kim,Harksoo Kim",
keywords = "question answering of medical documents,retrieval of candidate answer sentences,re-ranking of answer candidate sentences",
abstract = "With the explosive increase in the number of online medical documents, the demand for question-answering systems is increasing. Recently, question-answering models based on machine learning have shown high performances in various domains. However, many question-answering models within the medical domain are still based on information retrieval techniques because of sparseness of training data. Based on various information retrieval techniques, we propose an answer snippet retrieval model for question-answering systems of medical documents. The proposed model first searches candidate answer sentences from medical documents using a cluster-based retrieval technique. Then, it generates reliable answer snippets using a re-ranking model of the candidate answer sentences based on various sentence retrieval techniques. In the experiments with BioASQ 4b, the proposed model showed better performances (MAP of 0.0604) than the previous models."
}