Finding related sentence pairs in MEDLINE

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...

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Detalles Bibliográficos
Autores principales: Smith, Larry H., Wilbur, W. John
Formato: Texto
Lenguaje:English
Publicado: Springer Netherlands 2010
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2992462/
https://www.ncbi.nlm.nih.gov/pubmed/21170415
http://dx.doi.org/10.1007/s10791-010-9126-8
Descripción
Sumario: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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