Information Extraction Using Topic Models

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Topic Modeling is an information retrieval technique to identify topics in a large corpus of text documents. This tutorial will guide you through the process of analyzing your textual data right from pre-processing raw data - applying topic modeling algorithms - evaluating manually and automatically - analyzing using visualizations and its applications in few NLP tasks: Discovering topic correlation (with dendrograms), Document clustering (demo with Tensorboard), Document analysis (using word coloring). We will also cover the variants of topic models like dynamic topic model that learns the evolution of topics in a corpus collected over different time-stamps, and author topic model that learns the topic representations of authors in a corpus. See tutorial materials here: https://scipy2018.scipy.org/ehome/299527/648136/

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