Paper: Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models

ACL ID N13-1017
Title Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Multi-dimensional latent text models, such as factorial LDA (f-LDA), capture multiple fac- tors of corpora, creating structured output for researchers to better understand the contents of a corpus. We consider such models for clinical research of new recreational drugs and trends, an important application for mining current information for healthcare workers. We use a ?three-dimensional? f-LDA variant to jointly model combinations of drug (mari- juana, salvia, etc.), aspect (effects, chemistry, etc.) and route of administration (smoking, oral, etc.) Since a purely unsupervised topic model is unlikely to discover these specific factors of interest, we develop a novel method of incorporating prior knowledge by leverag- ing user generated tags as priors in our model. We demonstrate that this ...