Natural Language Processing and Text Mining
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With the increasing importance of the Web and other text-heavy application areas, the demands for and interest in both text mining and natural language processing (NLP) have been rising. Researchers in text mining have hoped that NLP—the attempt to extract a fuller meaning representation from free text—can provide useful improvements to text mining applications of all kinds.Bringing together a variety of perspectives from internationally renowned researchers, Natural Language Processing and Text Mining not only discusses applications of certain NLP techniques to certain Text Mining tasks, but also the converse, i.e., use of Text Mining to facilitate NLP. It explores a variety of real-world applications of NLP and text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, and detailing the associated difficulties that must be resolved before the algorithm can be applied and its full benefits realized. In addition, it explores a number of cutting-edge techniques and approaches, as well as novel ways of integrating various technologies. Nevertheless, even readers with only a basic knowledge of data mining or text mining will benefit from the many illustrative examples and solutions.Topics and features:• Describes novel and high-impact text mining and/or natural language applications• Points out typical traps in trying to apply NLP to text mining• Illustrates preparation and preprocessing of text data – offering practical issues and examples• Surveys related supporting techniques, problem types, and potential technique enhancements• Examines the interaction of text mining and NLPThis state-of-the-art, practical volume will be an essential resource for professionals and researchers who wish to learn how to apply text mining and language processing techniques to real world problems. In addition, it can be used as a supplementary text for advanced students studying text mining and NLP.
Overview Anne Kao Stephen R. Poteet 1Extracting Product Features and Opinions from Reviews Ana-Maria Popescu Oren Etzioni 9Extracting Relations from Text: From Word Sequences to Dependency Paths Razvan C. Bunescu Raymond J. Mooney 29Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles Eni Mustafaraj Martin Hoof Bernd Freisleben 45A Case Study in Natural Language Based Web Search Giovanni Marchisio Navdeep Dhillon Jisheng Liang Carsten Tusk Krzysztof Koperski Thien Nguyen Dan White Lubos Pochman 69Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models Chutima Boonthum Irwin B. Levinstein Danielle S. McNamara 91Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures Philip M. McCarthy Stephen W. Briner Vasile Rus Danielle S. McNamara 107Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling Mauritius A. R. Schmidtler Jan W. Amtrup 123Evolving Explanatory Novel Patterns for Semantically-Based Text Mining John Atkinson 145Handling of Imbalanced Data in Text Classification: Category-Based Term Weights Ying Liu Han Tong Loh Kamal Youcef-Toumi Shu Beng Tor 171Automatic Evaluation of Ontologies Janez Brank Marko Grobelnik Dunja Mladenic 193Linguistic Computing with UNIX Tools Lothar M. Schmitt Kiel Christianson Renu Gupta 221Index 259
