Interactive Topic Modeling

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 248–257, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Interactive Topic Modeling Yuening Hu Department of Computer Science University of Maryland ynhu@cs.umd.edu Jordan Boyd-Graber iSchool University of Maryland jbg@umiacs.umd.edu Brianna Satinoff Department of Computer Science University of Maryland bsonrisa@cs.umd.edu Abstract Topic models have been used extensively as a tool for corpus exploration, and a cottage in- dustry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavail- able to potential end-users of topic modeling software. In this work, we develop a frame- work for allowing users to iteratively refine the topics discovered by models such as la- tent Dirichlet allocation (LDA) by adding con- straints that enforce that sets of words must ap- pear together in the same topic. We incorporate these constraints interactively by selectively removing elements in the state of a Markov Chain used for inference; we investigate a va- riety of methods for incorporating this infor- mation and demonstrate that these interactively added constraints improve topic usefulness for simulated and actual user sessions. 1 Introduction Probabilistic topic models, as exemplified by prob- abilistic latent semantic indexing (Hofmann, 1999) and latent Dirichlet allocation (LDA) (Blei et al., 2003) are unsupervised statistical techniques to dis- cover the thematic topics that permeate a large cor- pus of text documents. Topic models have had con- siderable application beyond natural language pro- cessing in computer vision (Rob et al., 2005), bi- ology (Shringarpure and Xing, 2008), and psychol- ogy (Landauer et al., 2006) in addition to their canon- ical application to text. For text, one of the few real-world applications of topic models is corpus exploration. Unannotated, noisy, and ever-growing corpora are the norm rather than the exception, and topic models offer a way to quickly get the gist a large corpus.1 1For examples, see Rexa http://rexa.info/, JSTOR Contrary to the impression given by the tables shown in topic modeling papers, topics discovered by topic modeling don’t always make sense to os- tensible end users. Part of the problem is that the objective function of topic models doesn’t always cor- relate with human judgements (Chang et al., 2009). Another issue is that topic models — with their bag- of-words vision of the world — simply lack the nec- essary information to create the topics as end-users expect. There has been a thriving cottage industry adding more and more information to topic models to cor- rect these shortcomings; either by modeling perspec- tive (Paul and Girju, 2010; Lin et al., 2006), syn- tax (Wallach, 2006; Gruber et al., 2007), or author- ship (Rosen-Zvi et al., 2004; Dietz et al., 2007). Sim- ilarly, there has been an effort to inject human knowl- edge into topic models (Boyd-Graber et al., 2007; Andrzejewski et al., 2009; Petterson et al., 2010). However, these are a priori fixes. They don’t help a frustrated consumer of topic models …

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