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Mining Text Data ------SPRINGER.pdf

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    Mining Text Data
    Contents
    Preface
    Chapter 1 : AN INTRODUCTION TO TEXT MINING
    Chapter 2 : INFORMATION EXTRACTION FROM TEXT
    Chapter 3 : A SURVEY OF TEXT SUMMARIZATION TECHNIQUES
    Chapter 4 : A SURVEY OF TEXT CLUSTERING ALGORITHMS
    Chapter 5 : DIMENSIONALITY REDUCTION AND TOPIC MODELING: FROM LATENT SEMANTIC INDEXING TO LATENT DIRICHLET ALLOCATION AND BEYOND
    Chapter 6 : A SURVEY OF TEXT CLASSIFICATION ALGORITHMS
    Chapter 7: TRANSFER LEARNING FOR TEXT MINING
    Chapter 8: PROBABILISTIC MODELS FOR TEXT MINING
    Chapter 9: MINING TEXT STREAMS
    Chapter 10: TRANSLINGUAL MINING FROM TEXT DATA
    Chapter 11: TEXT MINING IN MULTIMEDIA
    Chapter 12: TEXT ANALYTICS IN SOCIAL MEDIA
    Chapter 13: A SURVEY OF OPINION MINING AND SENTIMENT ANALYSIS
    Chapter 14: BIOMEDICAL TEXT MINING: A SURVEY OF RECENT PROGRESS
    Index
    Mining Text Data
    Charu C. Aggarwal • ChengXiang Zhai Editors Mining Text Data
    Library of Congress Control Number: 2012930923 Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use inconnection with any form of information storage and retrieval, electronic adaptation, computer software,or by similar or dissimilar methodology now known or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if theyare not identified as such, is not to be taken as an expression of opinion as to whether or not they aresubject to proprietary rights. Springer New York Dordrecht Heidelberg LondonISBN978-1-4614-3222-7e-ISBN978-1-4614-3223-4DOI10.1007/978-1-4614-© Springer Science+Business Media, LLC 2012EditorsCharu C. AggarwalIBM T.J. Watson Research CenterYorktown Heights, NY, USAcharu@us.ibm.comUniversity of Illinois at Urbana-ChampaignUrbana, IL, USAczhai@cs.uiuc.edu3223-4ChengXiang Zhai
    Contents1AnIntroductiontoTextMining1CharuC.AggarwalandChengXiangZhai1.Introduction12.AlgorithmsforTextMining43.FutureDirections8References102InformationExtractionfromText11JingJiang1.Introduction112.NamedEntityRecognition152.1Rule-basedApproach162.2StatisticalLearningApproach173.RelationExtraction223.1Feature-basedClassification233.2KernelMethods263.3WeaklySupervisedLearningMethods294.UnsupervisedInformationExtraction304.1RelationDiscoveryandTemplateInduction314.2OpenInformationExtraction325.Evaluation336.ConclusionsandSummary34References353ASurveyofTextSummarizationTechniques43AniNenkovaandKathleenMcKeown1.HowdoExtractiveSummarizersWork?442.TopicRepresentationApproaches462.1TopicWords462.2Frequency-drivenApproaches482.3LatentSemanticAnalysis522.4BayesianTopicModels532.5SentenceClusteringandDomain-dependentTopics553.InfluenceofContext563.1WebSummarization573.2SummarizationofScientificArticles58v
    for Summarization 2. Feature Selection and Transformation Methods for Text Clustering 81 5 Dimensionality Reduction and Topic Modeling Steven P. Crain, Ke Zhou, Shuang-Hong Yang and Hongyuan Zha 1. 2. 3. 4. Introduction 1.1 The Relationship Between Clustering, Dimension Reduction and Topic Modeling Notation and Concepts The Procedure of Latent Semantic Indexing Implementation Issues Analysis 1.2 Latent Semantic Indexing 2.1 2.2 2.3 Topic Models and Dimension Reduction 3.1 3.2 Interpretation and Evaluation Probabilistic Latent Semantic Indexing Latent Dirichlet Allocation 129 130 131 132 133 134 135 137 139 140 142 148 viMININGTEXTDATA3.3Query-focusedSummarization583.4EmailSummarization594.IndicatorRepresentationsandMachineLearning4.1GraphMethodsforSentenceImportance604.2MachineLearningforSummarization625.SelectingSummarySentences645.1GreedyApproaches:MaximalMarginalRelevance645.2GlobalSummarySelection656.Conclusion66References664ASurveyofTextClusteringAlgorithms77CharuC.AggarwalandChengXiangZhai1.Introduction772.1FeatureSelectionMethods812.2LSI-basedMethods842.3Non-negativeMatrixFactorization863.Distance-basedClusteringAlgorithms893.1AgglomerativeandHierarchicalClusteringAlgorithms903.2Distance-basedPartitioningAlgorithms923.3AHybridApproach:TheScatter-GatherMethod944.WordandPhrase-basedClustering994.1ClusteringwithFrequentWordPatterns1004.2LeveragingWordClustersforDocumentClusters1024.3Co-clusteringWordsandDocuments1034.4ClusteringwithFrequentPhrases1055.ProbabilisticDocumentClusteringandTopicModels1076.OnlineClusteringwithTextStreams1107.ClusteringTextinNetworks1158.Semi-SupervisedClustering1189.ConclusionsandSummary120References12160
    Contentsvii4.1Interpretation1484.2Evaluation1494.3ParameterSelection1504.4DimensionReduction1505.BeyondLatentDirichletAllocation1515.1Scalability1515.2DynamicData1515.3NetworkedData1525.4AdaptingTopicModelstoApplications1546.Conclusion155References1566ASurveyofTextClassificationAlgorithms163CharuC.AggarwalandChengXiangZhai1.Introduction1632.FeatureSelectionforTextClassification1672.1GiniIndex1682.2InformationGain1692.3MutualInformation1692.4χ2-Statistic1702.5FeatureTransformationMethods:SupervisedLSI1712.6SupervisedClusteringforDimensionalityReduction1722.7LinearDiscriminantAnalysis1732.8GeneralizedSingularValueDecomposition1752.9InteractionofFeatureSelectionwithClassification1753.DecisionTreeClassifiers1764.Rule-basedClassifiers1785.ProbabilisticandNaiveBayesClassifiers1815.1BernoulliMultivariateModel1835.2MultinomialDistribution1885.3MixtureModelingforTextClassification1906.LinearClassifiers1936.1SVMClassifiers1946.2Regression-BasedClassifiers1966.3NeuralNetworkClassifiers1976.4SomeObservationsaboutLinearClassifiers1997.Proximity-basedClassifiers2008.ClassificationofLinkedandWebData2039.Meta-AlgorithmsforTextClassification2099.1ClassifierEnsembleLearning2099.2DataCenteredMethods:BoostingandBagging2109.3OptimizingSpecificMeasuresofAccuracy21110.ConclusionsandSummary213References2137TransferLearningforTextMining223WeikePan,ErhengZhongandQiangYang1.Introduction2242.TransferLearninginTextClassification2252.1CrossDomainTextClassification225
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