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- Title
Interactive topic modeling.
- Authors
Hu, Yuening; Boyd-Graber, Jordan; Satinoff, Brianna; Smith, Alison
- Abstract
Topic models are a useful and ubiquitous tool for understanding large corpora. However, topic models are not perfect, and for many users in computational social science, digital humanities, and information studies-who are not machine learning experts-existing models and frameworks are often a 'take it or leave it' proposition. This paper presents a mechanism for giving users a voice by encoding users' feedback to topic models as correlations between words into a topic model. This framework, interactive topic modeling ( itm), allows untrained users to encode their feedback easily and iteratively into the topic models. Because latency in interactive systems is crucial, we develop more efficient inference algorithms for tree-based topic models. We validate the framework both with simulated and real users.
- Subjects
UBIQUITOUS computing; DIGITAL humanities; INFORMATION theory; MACHINE learning; COMPUTER users; CODING theory
- Publication
Machine Learning, 2014, Vol 95, Issue 3, p423
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-013-5413-0