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A popular approach to topic modeling involves extracting co-occurring n-gramsof a corpus into semantic themes. The set of n-grams in a theme represents anunderlying topic, but most topic modeling approaches are not able to labelthese sets of words with a single n-gram. Such labels are useful for topicidentification in summarization systems. This paper introduces a novel approachto labeling a group of n-grams comprising an individual topic. The approachtaken is to complement the existing topic distributions over words with a knowndistribution based on a predefined set of topics. This is done by integratingexisting labeled knowledge sources representing known potential topics into theprobabilistic topic model. These knowledge sources are translated into adistribution and used to set the hyperparameters of the Dirichlet generateddistribution over words. In the inference these modified distributions guidethe convergence of the latent topics to conform with the complementarydistributions. This approach ensures that the topic inference process isconsistent with existing knowledge. The label assignment from the complementaryknowledge sources are then transferred to the latent topics of the corpus. Theresults show both accurate label assignment to topics as well as improved topicgeneration than those obtained using various labeling approaches based offLatent Dirichlet allocation (LDA).

Source-LDA: Enhancing probabilistic topic models using prior knowledge sources