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Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web.

Information Extraction --- Data Mining --- Pattern Induction --- Web Mining --- Machine Learning

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Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes—including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods—the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge. Readers will learn to: • appreciate the role of data mining in scientific research • develop an understanding of fundamental concepts of data mining and knowledge discovery • use software to carry out data mining tasks • select and assess appropriate models to ensure findings are valid and meaningful • develop basic skills in data preparation, data mining, model selection, and validation • apply concepts with end-of-chapter exercises and review summaries

Social sciences --- Research --- Data processing. --- academic research. --- academic. --- anova test. --- best fit model. --- box plot. --- causality. --- classification. --- collecting data. --- data mining. --- data processing. --- decision trees. --- matrix. --- regression. --- scholarly research. --- scholarly. --- scientific research. --- scientific study. --- social science. --- statistical analysis. --- statistics. --- text mining. --- variables. --- web mining.