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Improve Biomedical Information Retrieval Using Modified Learning to Rank Methods | IEEE Journals & Magazine | IEEE Xplore

Improve Biomedical Information Retrieval Using Modified Learning to Rank Methods


Abstract:

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Info...Show More

Abstract:

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.
Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Volume: 15, Issue: 6, 01 Nov.-Dec. 2018)
Page(s): 1797 - 1809
Date of Publication: 14 June 2016

ISSN Information:

PubMed ID: 27323371

Funding Agency:


References

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