The proliferation of digital computing devices and their use in communication has resulted in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully structured textual data has become important in both academia and industry. This 2nd edition surveys the emerging field of Text Mining - the application of techniques of machine learning in conjunction with natural language processing, information extraction and algebraic/mathematical approaches to computational information retrieval. Many issues are addressed, ranging from the development of new learning approaches to the parallelization of existing algorithms. Presenting a comprehensive selection of topics within the field to generate both interest and insight into the state of Text Mining, this book will be an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining.

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