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论文类型：期刊论文

发表时间：2018-11-01

发表刊物：IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

收录刊物：SCIE、Scopus

卷号：15

期号：6

页面范围：1797-1809

ISSN号：1545-5963

关键字：Information retrieval; machine learning; supervised learning; text mining

摘要：In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.