Text Mining with Probabilistic Topic Models: Applications in Information Retrieval and Concept Modeling,Used

Text Mining with Probabilistic Topic Models: Applications in Information Retrieval and Concept Modeling,Used

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SKU: DADAX3838364104
Brand: LAP Lambert Academic Publishing
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Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the specialwords topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a specialwords distribution specific to the document, and (iii) a corpuslevel background distribution. We describe the utility of the specialwords topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine datadriven topics and semantic concepts, we describe the concepttopic model and the hierarchical concepttopic model which represent a document as a distribution over datadriven topics and semantic concepts.

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