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MACHINE LEARNING FOR TEXT IBD

CHARU C AGGARWAL
SPRINGER
02 / 2019
9783030088071
Inglés

Sinopsis

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbookácarefully covers a coherently organized framework drawn from these intersectingátopics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithmsáfor machine learning from text such as preprocessing, similarityácomputation, topic modeling, matrix factorization, clustering,áclassification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methodsáfrom text when combined with different domains such as multimedia andáthe Web. The problem of information retrieval and Web search is alsoádiscussed in the context of its relationship with ranking and machineálearning methods.á- Sequence-centric mining: Chapters 10 through 14 discuss variousásequence-centric and natural language applications, such as featureáengineering, neural language models, deep learning, text summarization,áinformation extraction, opinion mining, text segmentation, and eventádetection.áThis textbook covers machine learning topics for text in detail. Since theácoverage is extensive,multiple courses can be offered from the same book,ádepending on course level. Even though the presentation is text-centric,áChapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offerácourses not just in text analytics but also from the broader perspective ofámachine learning (with text as a backdrop).áThis textbook targets graduate students in computer science, as well as researchers, professors, and industrialápractitioners working in these related fields. This textbook is accompanied with a solution manual foráclassroom teaching.

PVP
86,81€