Precision-Recall Balanced Topic Modelling

NeurIPS 2019 Seppo VirtanenMark Girolami

Topic models are becoming increasingly relevant probabilistic models for dimensionality reduction of text data, inferring topics that capture meaningful themes of frequently co-occurring terms. We formulate topic modelling as an information retrieval task, where the goal is, based on the latent topic representation, to capture relevant term co-occurrence patterns... (read more)

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