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In this paper we describe a novel framework for the discovery of the topicalcontent of a data corpus, and the tracking of its complex structural changesacross the temporal dimension. In contrast to previous work our model does notimpose a prior on the rate at which documents are added to the corpus nor doesit adopt the Markovian assumption which overly restricts the type of changesthat the model can capture. Our key technical contribution is a framework basedon (i) discretization of time into epochs, (ii) epoch-wise topic discoveryusing a hierarchical Dirichlet process-based model, and (iii) a temporalsimilarity graph which allows for the modelling of complex topic changes:emergence and disappearance, evolution, and splitting and merging. The power ofthe proposed framework is demonstrated on the medical literature corpusconcerned with the autism spectrum disorder (ASD) - an increasingly importantresearch subject of significant social and healthcare importance. In additionto the collected ASD literature corpus which we will make freely available, ourcontributions also include two free online tools we built as aids to ASDresearchers. These can be used for semantically meaningful navigation andsearching, as well as knowledge discovery from this large and rapidly growingcorpus of literature.

Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature