Finding related sentence pairs in MEDLINE |
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| Authors: | Larry H Smith W John Wilbur |
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| Institution: | (1) Computational Biology Branch, National Center for Biotechnology Information, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA |
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| Abstract: | We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning.
We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine
learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function,
achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average.
We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts
by this automatic procedure. |
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