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Automatic Indexing of Journal Abstracts with Latent Semantic Analysis

  • Conference paper
  • First Online:
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9283))

Abstract

The BioASQ “Task on Large-Scale Online Biomedical Semantic Indexing” charges participants with assigning semantic tags to biomedical journal abstracts. We present a system that takes as input a biomedical abstract and uses latent semantic analysis to identify similar documents in the MEDLINE database. The system then uses a novel ranking scheme to select a list of MeSH tags from candidates drawn from the most similar documents. Our approach achieved better than baseline performance in both precision and recall. We suggest several possible strategies to improve the system’s performance.

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Correspondence to Joel Robert Adams .

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Adams, J.R., Bedrick, S. (2015). Automatic Indexing of Journal Abstracts with Latent Semantic Analysis. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_17

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  • DOI: https://doi.org/10.1007/978-3-319-24027-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24026-8

  • Online ISBN: 978-3-319-24027-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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