文件名称：Machine Learning for Text
文件大小：5.98MB
文件格式：RAR
更新时间：2022-02-22 17:10:32
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Machine Learning for Text By 作者: Charu C. Aggarwal ISBN-10 书号: 3319735306 ISBN-13 书号: 9783319735306 Edition 版本: 1st ed. 出版日期: 2018-03-20 pages 页数: (493 ) Springer 出版超清 $79.99 Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: – Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. – Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. – Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching. Frontmatter 1.Machine Learning for Text:An Introduction 2.Text Preparation and Similarity Computation 3.Matrix Factorization and Topic Modeling 4.Text Clustering 5.Text Classification:Basic Models 6.Linear Classification and Reression for Text 7.Classifier Performance and Evaluation 8.Joint Text Mining with Heterogeneous Data 9.Information Retrieval and Search Engines 10.Text Sequence Modeling and Deep Learning 11.Text Summarization 12.Information Extraction 13.Opinion Mining and Sentiment Analysis 14.Text Segmentation and Event Detection Backmatter
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Machine Learning for Text.pdf