Название: Text Data MiningАвтор: Chengqing Zong, Rui Xia
Издательство: Springer, Tsinghua University Press
Год: 2021
Страниц: 363
Язык: английский
Формат: pdf (true), epub
Размер: 25.7 MB
Focuses on text data mining from an NLP perspective.
This book discusses various aspects of text data mining. Unlike other books that focus on Machine Learning (ML) or databases, it approaches text data mining from a natural language processing (NLP) perspective.
Text mining is a confluence of natural language processing, data mining, Machine Learning, and statistics used to mine knowledge from unstructured text. There have already been multiple textbooks dedicated to data mining, Machine Learning, statistics, and natural language processing. However, we seriously lack textbooks on text mining that systematically introduce important topics and up-to-date methods for text mining. This book, “Text Data Mining,” bridges this gap nicely. It is the first textbook, and a brilliant one, on text data mining that not only introduces foundational issues but also offers comprehensive and state-of-the-art coverage of the important and ongoing research themes on text mining. The in-depth treatment of a wide spectrum of text mining themes and clear introduction to the state-of-the-art deep learning methods for text mining make the book unique, timely, and authoritative. It is a great textbook for graduate students as well as a valuable handbook for practitioners working on text mining, natural language processing, data mining, and Machine Learning and their applications.
The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview.
Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.
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