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Probabilistic topic modelsfoundation and application /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic topic modelsby Di Jiang, Chen Zhang, Yuanfeng Song.
其他題名:	foundation and application /
作者:	Jiang, Di.
其他作者:	Zhang, Chen.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	x, 149 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Natural Language Processing (NLP)
電子資源:	https://doi.org/10.1007/978-981-99-2431-8
ISBN:	9789819924318$q(electronic bk.)

Probabilistic topic modelsfoundation and application /
Jiang, Di.

Probabilistic topic modelsfoundation and application /[electronic resource] :by Di Jiang, Chen Zhang, Yuanfeng Song. - Singapore :Springer Nature Singapore :2023. - x, 149 p. :ill., digital ;24 cm.

Chapter 1. Basics -- Chapter 2. Topic Models -- 3. Chapter 3. Pre-processing of Training Data -- Chapter 4. Expectation Maximization -- Chapter 5. Markov Chain Monte Carlo Sampling -- Chapter 6. Variational Inference -- Chapter 7. Distributed Training -- Chapter 8. Parameter Setting -- Chapter 9. Topic Deduplication and Model Compression -- Chapter 10. Applications.

This book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role in both academia and industry. This book offers a valuable reference guide for senior undergraduate students, graduate students, and researchers, covering the latest advances in topic models, and for industrial practitioners, sharing state-of-the-art solutions for topic-related applications. The book can also serve as a reference for job seekers preparing for interviews.

ISBN: 9789819924318$q(electronic bk.)

Standard No.: 10.1007/978-981-99-2431-8doiSubjects--Topical Terms:

826373
Natural Language Processing (NLP)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Probabilistic topic modelsfoundation and application /
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520 $a This book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role in both academia and industry. This book offers a valuable reference guide for senior undergraduate students, graduate students, and researchers, covering the latest advances in topic models, and for industrial practitioners, sharing state-of-the-art solutions for topic-related applications. The book can also serve as a reference for job seekers preparing for interviews.
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