Automatic classification of PubMed abstracts with Latent semantic indexing: Working notes

Joel Robert Adams, Steven Bedrick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The 2014 BioASQ challenge 2a tasks participants with assigning semantic tags to biomedical journal abstracts. We present a system that uses Latent Semantic Analysis to identify semantically similar documents in MEDLINE to an unlabeled abstract, and then uses a novel ranking scheme to select a list of MeSH headers from candidates drawn from the most similar documents. Our approach achieved good precision, but suffered in terms of recall. We describe several possible strategies to improve our system's performance.

Original languageEnglish (US)
Title of host publicationCLEF 2014 - Working Notes for CLEF 2014 Conference
PublisherCEUR-WS
Pages1275-1282
Number of pages8
Volume1180
StatePublished - 2014
Event2014 Cross Language Evaluation Forum Conference, CLEF 2014 - Sheffield, United Kingdom
Duration: Sep 15 2014Sep 18 2014

Other

Other2014 Cross Language Evaluation Forum Conference, CLEF 2014
CountryUnited Kingdom
CitySheffield
Period9/15/149/18/14

ASJC Scopus subject areas

  • Computer Science(all)

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    Adams, J. R., & Bedrick, S. (2014). Automatic classification of PubMed abstracts with Latent semantic indexing: Working notes. In CLEF 2014 - Working Notes for CLEF 2014 Conference (Vol. 1180, pp. 1275-1282). CEUR-WS.