文本数据挖掘(英文版)

1 Introduction 1 1.1 The Basic Concepts 1 1.2 Main Tasks of Text Data Mining 3 1.3 Existing Challenges in Text Data Mining 6 1.4 Overview and Organization of This Book 9 1.5 Further Reading 12 2 Data Annotation and Preprocessing 15 2.1 Data Acquisition 15 2.2 Data Preprocessing 20 2.3 Data Annotation 22 2.4 Basic Tools of NLP 25 2.4.1 Tokenization and POS Tagging 25 2.4.2 Syntactic Parser 27 2.4.3 N-gram Language Model 29 2.5 Further Reading 30 3 Text Representation 33 3.1 Vector Space Model 33 3.1.1 Basic Concepts 33 3.1.2 Vector Space Construction 34 3.1.3 Text Length Normalization 36 3.1.4 Feature Engineering 37 3.1.5 Other Text Representation Methods 39 3.2 Distributed Representation of Words 40 3.2.1 Neural Network Language Model 41 3.2.2 C&W Model 45 3.2.3 CBOW and Skip-Gram Model 47 3.2.4 Noise Contrastive Estimation and Negative Sampling 49 3.2.5 Distributed Representation Based on the Hybrid Character-Word Method 51 3.3 Distributed Representation of Phrases 53 3.3.1 Distributed Representation Based on the Bag-of-Words Model 54 3.3.2 Distributed Representation Based on Autoencoder 54 3.4 Distributed Representation of Sentences 58 3.4.1 General Sentence Representation 59 3.4.2 Task-Oriented Sentence Representation 63 3.5 Distributed Representation of Documents 66 3.5.1 General Distributed Representation of Documents 67 3.5.2 Task-Oriented Distributed Representation of Documents 69 3.6 Further Reading 72 4 Text Representation with Pretraining and Fine-Tuning 75 4.1 ELMo: Embeddings from Language Models 75 4.1.1 Pretraining Bidirectional LSTM Language Models 76 4.1.2 Contextualized ELMo Embeddings for Downstream Tasks 77 4.2 GPT: Generative Pretraining 78 4.2.1 Transformer 78 4.2.2 Pretraining the Transformer Decoder 80 4.2.3 Fine-Tuning the Transformer Decoder 81 4.3 BERT: Bidirectional Encoder Representations from Transformer 82 4.3.1 BERT: Pretraining 83 4.3.2 BERT: Fine-Tuning 86 4.3.3 XLNet: Generalized Autoregressive Pretraining 86 4.3.4 UniLM 89 4.4 Further Reading 90 5 Text Classi?cation 93 5.1 The Traditional Framework of Text Classi?cation 93 5.2 Feature Selection 95 5.2.1 Mutual Information 96 5.2.2 Information Gain 99 5.2.3 The Chi-Squared Test Method 100 5.2.4 Other Methods 101 5.3 Traditional Machine Learning Algorithms for Text Classi?cation 102 5.3.1 Na?ve Bayes 103 5.3.2 Logistic/Softmax and Maximum Entropy 105 5.3.3 Support Vector Machine 107 5.3.4 Ensemble Methods 110 5.4 Deep Learning Methods ............................................. 111 5.4.1 Multilayer Feed-Forward Neural Network ................ 111 5.4.2 Convolutional Neural Network ............................ 113 5.4.3 Recurrent Neural Network ................................. 115 5.5 Evaluation of Text Classi?cation 120 5.6 Further Reading 123 6 Text Clustering 125 6.1 Text Similarity Measures 125 6.1.1 The Similarity Between Documents 125 6.1.2 The Similarity Between Clusters 128 6.2 Text Clustering Algorithms 129 6.2.1 K-Means Clustering 129 6.2.2 Single-Pass Clustering 133 6.2.3 Hierarchical Clustering 136 6.2.4 Density-Based Clustering 138 6.3 Evaluation of Clustering 141 6.3.1 External Criteria 141 6.3.2 Internal Criteria 142 6.4 Further Reading 143 7 Topic Model 145 7.1 The History of Topic Modeling. 145 7.2 Latent Semantic Analysis 146 7.2.1 Singular Value Decomposition of the Term-by-Document Matrix 147 7.2.2 Conceptual Representation and Similarity Computation 148 7.3 Probabilistic Latent Semantic Analysis 150 7.3.1 Model Hypothesis .......................................... 150 7.3.2 Parameter Learning ......................................... 151 7.4 Latent Dirichlet Allocation .......................................... 153 7.4.1 Model Hypothesis .......................................... 153 7.4.2 Joint Probability ............................................ 155 7.4.3 Inference in LDA ........................................... 158 7.4.4 Inference for New Documents ............................. 160 7.5 Further Reading 161 8 Sentiment Analysis and Opinion Mining 163 8.1 History of Sentiment Analysis and Opinion Mining 163 8.2 Categorization of Sentiment Analysis Tasks 164 8.2.1 Categorization According to Task Output 164 8.2.2 According to Analysis Granularity 165 8.3 Methods for Document/Sentence-Level Sentiment Analysis 168 8.3.1 Lexicon- and Rule-Based Methods 169 8.3.2 Traditional Machine Learning Methods 170 8.3.3 Deep Learning Methods 174 8.4 Word-Level Sentiment Analysis and Sentiment Lexicon Construction 178 8.4.1 Knowledgebase-Based Methods 178 8.4.2 Corpus-Based Methods 179 8.4.3 Evaluation of Sentiment Lexicons 182 8.5 Aspect-Level Sentiment Analysis 183 8.5.1 Aspect Term Extraction .................................... 183 8.5.2 Aspect-Level Sentiment Classi?cation .................... 186 8.5.3 Generative Modeling of Topics and Sentiments .......... 191 8.6 Special Issues in Sentiment Analysis................................ 193 8.6.1 Sentiment Polarity Shift .................................... 193 8.6.2 Domain Adaptation ......................................... 195 8.7 Further Reading ...................................................... 198 9 Topic Detection and Tracking ............................................. 201 9.1 History of Topic Detection and Tracking ........................... 201 9.2 Terminology and Task De?nition.................................... 202 9.2.1 Terminology ................................................ 202 9.2.2 Task ......................................................... 203 9.3 Story/Topic Representation and Similarity Computation .......... 206 9.4 Topic Detection....................................................... 209 9.4.1 Online Topic Detection ..................................... 209 9.4.2 Retrospective Topic Detection ............................. 211 9.5 Topic Tracking........................................................ 212 9.6 Evaluation ............................................................ 213 9.7 Social Media Topic Detection and Tracking ........................ 215 9.7.1 Social Media Topic Detection.............................. 216 9.7.2 Social Media Topic Tracking .............................. 217 9.8 Bursty Topic Detection............................................... 217 9.8.1 Burst State Detection ....................................... 218 9.8.2 Document-Pivot Methods .................................. 221 9.8.3 Feature-Pivot Methods ..................................... 222 9.9 Further Reading ...................................................... 224 10 Information Extraction 227 10.1 Concepts and History 227 10.2 Named Entity Recognition 229 10.2.1 Rule-based Named Entity Recognition 230 10.2.2 Supervised Named Entity Recognition Method 231 10.2.3 Semisupervised Named Entity Recognition Method 239 10.2.4 Evaluation of Named Entity Recognition Methods 241 10.3 Entity Disambiguation ............................................... 242 10.3.1 Clustering-Based Entity Disambiguation Method ........ 243 10.3.2 Linking-Based Entity Disambiguation .................... 248 10.3.3 Evaluation of Entity Disambiguation .. . . . ................. 254 10.4 Relation Extraction ................................................... 256 10.4.1 Relation Classi?cation Using Discrete Features .......... 258 10.4.2 Relation Classi?cation Using Distributed Features ....... 265 10.4.3 Relation Classi?cation Based on Distant Supervision .. . . 268 10.4.4 Evaluation of Relation Classi?cation . ..................... 269 10.5 Event Extraction 270 10.5.1 Event Description Template................................ 270 10.5.2 Event Extraction Method ................................... 272 10.5.3 Evaluation of Event Extraction ............................ 281 10.6 Further Reading ...................................................... 281 11 Automatic Text Summarization 285 11.1 Main Tasks in Text Summarization 285 11.2 Extraction-Based Summarization 287 11.2.1 Sentence Importance Estimation 287 11.2.2 Constraint-Based Summarization Algorithms 298 11.3 Compression-Based Automatic Summarization 299 11.3.1 Sentence Compression Method 300 11.3.2 Automatic Summarization Based on Sentence Compression 305 11.4 Abstractive Automatic Summarization 307 11.4.1 Abstractive Summarization Based on Information Fusion 307 11.4.2 Abstractive Summarization Based on the Encoder-Decoder Framework .............................. 313 11.5 Query-Based Automatic Summarization ............................ 316 11.5.1 Relevance Calculation Based on the Language Model . . . 317 11.5.2 Relevance Calculation Based on Keyword Co-occurrence .............................................. 317 11.5.3 Graph-Based Relevance Calculation Method ............. 318 11.6 Crosslingual and Multilingual Automatic Summarization ......... 319 11.6.1 Crosslingual Automatic Summarization .. . ................ 319 11.6.2 Multilingual Automatic Summarization .. . . ............... 323 11.7 Summary Quality Evaluation and Evaluation Workshops.......... 325 11.7.1 Summary Quality Evaluation Methods .................... 325 11.7.2 Evaluation Workshops...................................... 330 11.8 Further Reading ...................................................... 332 References 335