Using Topic Models to Interpret MEDLINE's Medical Subject Headings

Authors:
David Newman;Sarvnaz Karimi;Lawrence Cavedon
Affiliations:
NICTA and The University of Melbourne, Australia and University of California, Irvine, USA;NICTA and The University of Melbourne, Australia;NICTA and The University of Melbourne, Australia
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

Citing 8
Cited 1

Latent dirichlet allocation

The Journal of Machine Learning Research
Applying discrete PCA in data analysis

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The author-topic model for authors and documents

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Semantic taxonomy induction from heterogenous evidence

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Anticipating annotations and emerging trends in biomedical literature

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining concept hierarchies and statistical topic models

Proceedings of the 17th ACM conference on Information and knowledge management
Evaluation of query expansion using MeSH in PubMed

Information Retrieval
Modeling actions of PubMed users with n-gram language models

Information Retrieval

Topic models for taxonomies

Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries

Quantified Score

Hi-index	0.00

Visualization

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.