Paper: Markov Random Topic Fields

ACL ID P09-2074
Title Markov Random Topic Fields
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009

Most approaches to topic modeling as- sume an independence between docu- ments that is frequently violated. We present an topic model that makes use of one or more user-specified graphs de- scribing relationships between documents. These graph are encoded in the form of a Markov random field over topics and serve to encourage related documents to have similar topic structures. Experiments on show upwards of a 10% improvement in modeling performance.