Abstract
We consider the task of interpreting and understanding a taxonomy of classification terms applied to documents in a collection. In particular, we show how unsupervised topic models are useful for interpreting and understanding MeSH, the Medical Subject Headings applied to articles in MEDLINE. We introduce the resampled author model, which captures some of the advantages of both the topic model and the author-topic model. We demonstrate how topic models complement and add to the information conveyed in a traditional listing and description of a subject heading hierarchy.
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Newman, D., Karimi, S., Cavedon, L. (2009). Using Topic Models to Interpret MEDLINE’s Medical Subject Headings. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_28
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DOI: https://doi.org/10.1007/978-3-642-10439-8_28
Publisher Name: Springer, Berlin, Heidelberg
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