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In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms. In addition, we propose new probabilistic metrics inspired by the concepts...
In this paper we focus on the task of identifying topics in large text collections in a completely unsupervised way. In contrast to probabilistic topic modeling methods that require first estimating the density of probability distributions, we model topics as subsets of terms that are used as queries to an index of documents. By retrieving the documents relevant to those topical-queries we obtain...
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