_sfscon18-gabriele-sottocornola-probabilistic-topic-models-with-mallet.jpg "SFScon18 - Gabriele Sottocornola - Probabilistic Topic Models with MALLET from java themes Video Screenshot Preview")
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⏲ Duration: 7 min 92 sec
✓ Published: 09-Dec-2018
✓ Published: 09-Dec-2018
Description:
Probabilistic Topic Models (TMs) are a suite of statistical algorithms that aim to discover the main themes, denoted as topics, that pervade a large and otherwise unstructured collection of natural language documents. PTMs are able to annotate and summarize this corpus with the thematic and semantic information provided by topics. nThe most successful contribution in the field of PTMs is the Latent Dirichlet Allocation (LDA). LDA is based upon the idea that documents hide a mixture of multiple t
Probabilistic Topic Models (TMs) are a suite of statistical algorithms that aim to discover the main themes, denoted as topics, that pervade a large and otherwise unstructured collection of natural language documents. PTMs are able to annotate and summarize this corpus with the thematic and semantic information provided by topics. nThe most successful contribution in the field of PTMs is the Latent Dirichlet Allocation (LDA). LDA is based upon the idea that documents hide a mixture of multiple t
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