Description
Taylor & Francis Ltd Text Mining 2009 Edition by Ashok N. Srivastava, Mehran Sahami
The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search. The book begins with chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use.There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field. Analysis of Text Patterns Using Kernel MethodsMarco Turchi, Alessia Mammone, and Nello CristianiniIntroduction General Overview on Kernel Methods Kernels for TextExampleConclusion and Further ReadingDetection of Bias in Media Outlets with Statistical Learning MethodsBlaz Fortuna, Carolina Galleguillos, and Nello CristianiniIntroduction Overview of the Experiments Data Collection and Preparation News Outlet Identification Topic-Wise Comparison of Term Bias News Outlets Map Related Work ConclusionAppendix A: Support Vector Machines Appendix B: Bag of Words and Vector Space Models Appendix C: Kernel Canonical Correlation Analysis Appendix D: Multidimensional ScalingCollective Classification for Text ClassificationGalileo Namata, Prithviraj Sen, Mustafa Bilgic, and Lise GetoorIntroduction Collective Classification: Notation and Problem Definition Approximate Inference Algorithms for Approaches Based on Local Conditional ClassifiersApproximate Inference Algorithms for Approaches Based on Global Formulations Learning the ClassifiersExperimental Comparison Related Work Conclusion Topic ModelsDavid M. Blei and John D. LaffertyIntroduction Latent Dirichlet Allocation (LDA)Posterior Inference for LDA Dynamic Topic Models and Correlated Topic Models Discussion Nonnegative Matrix and Tensor Factorization for Discussion TrackingBrett W. Bader, Michael W. Berry, and Amy N. LangvilleIntroduction Notation Tensor Decompositions and Algorithms Enron Subset Observations and Results Visualizing Results of the NMF Clustering Future WorkText Clustering with Mixture of von Mises-Fisher DistributionsArindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, and Suvrit SraIntroduction Related Work Preliminaries EM on a Mixture of vMFs (moVMF) Handling High-Dimensional Text Datasets Algorithms Experimental Results Discussion Conclusions and Future Work Constrained Partitional Clustering of Text Data: An OverviewSugato Basu and Ian DavidsonIntroduction Uses of Constraints Text Clustering Partitional Clustering with Constraints Learning Distance Function with Constraints Satisfying Constraints and Learning Distance Functions Experiments Conclusions Adaptive Information FilteringYi ZhangIntroduction Standard Evaluation MeasuresStandard Retrieval Models and Filtering ApproachesCollaborative Adaptive FilteringNovelty and Redundancy DetectionOther Adaptive Filtering Topics Utility-Based Information DistillationYiming Yang and Abhimanyu LadIntroductionA Sample Task Technical Cores Evaluation Methodology Data Experiments and ResultsConcluding RemarksText Search Enhanced with Types and EntitiesSoumen Chakrabarti, Sujatha Das, Vijay Krishnan, and Kriti PuniyaniEntity-Aware Search Architecture Understanding the Question Scoring Potential Answer Snippets Indexing and Query Processing Conclusion Index