Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10439-8_28 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
medical subject,unsupervised topic model,interpret medline,classification term,topic models,traditional listing,topic model,author-topic model,medical subject headings,resampled author model,natural language processing,subject headings | Latent Dirichlet allocation,Query expansion,Information retrieval,Computer science,Artificial intelligence,Natural language processing,Topic model,Hierarchy,MEDLINE | Conference |
Volume | ISSN | Citations |
5866 | 0302-9743 | 10 |
PageRank | References | Authors |
0.58 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Newman | 1 | 1319 | 73.72 |
| Sarvnaz Karimi | 2 | 380 | 33.01 |
| Lawrence Cavedon | 3 | 10 | 0.58 |