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The Journal of Machine Learning Research
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This paper explores correspondence and mixture topic modeling of documents tagged from two different perspectives. There has been ongoing work in topic modeling of documents with tags (tag-topic models) where words and tags typically reflect a single perspective, namely document content. However, words in documents can also be tagged from different perspectives, for example, syntactic perspective as in part-of-speech tagging or an opinion perspective as in sentiment tagging. The models proposed in this paper are novel in: (i) the consideration of two different tag perspectives -- a document level tag perspective that is relevant to the document as a whole and a word level tag perspective pertaining to each word in the document; (ii) the attribution of latent topics with word level tags and labeling latent topics with images in case of multimedia documents; and (iii) discovering the possible correspondence of the words to document level tags. The proposed correspondence tag-topic model shows better predictive power i.e. higher likelihood on heldout test data than all existing tag topic models and even a supervised topic model. To evaluate the models in practical scenarios, quantitative measures between the outputs of the proposed models and the ground truth domain knowledge have been explored. Manually assigned (gold standard) document category labels in Wikipedia pages are used to validate model-generated tag suggestions using a measure of pairwise concept similarity within an ontological hierarchy like WordNet. Using a news corpus, automatic relationship discovery between person names was performed and compared to a robust baseline.