MACHINE LEARNING FOR TEXT

Preface
Acknowledgments
Contents
Author Biography
1 An Introduction to Text Analytics
	1.1 Introduction
	1.2 What Is Special About Learning from Text?
	1.3 Analytical Models for Text
		1.3.1 Text Preprocessing and Similarity Computation
		1.3.2 Dimensionality Reduction and Matrix Factorization
		1.3.3 Text Clustering
			1.3.3.1 Deterministic and Probabilistic Matrix FactorizationMethods
			1.3.3.2 Probabilistic Mixture Models of Documents
			1.3.3.3 Similarity-Based Algorithms
			1.3.3.4 Advanced Methods
		1.3.4 Text Classification and Regression Modeling
			1.3.4.1 Decision Trees
			1.3.4.2 Rule-Based Classifiers
			1.3.4.3 Naïve Bayes Classifier
			1.3.4.4 Nearest Neighbor Classifiers
			1.3.4.5 Linear Classifiers
			1.3.4.6 Broader Topics in Classification
		1.3.5 Joint Analysis of Text with Heterogeneous Data
		1.3.6 Information Retrieval and Web Search
		1.3.7 Sequential Language Modeling and Embeddings
		1.3.8 Transformers and Pretrained Language Models
		1.3.9 Text Summarization
		1.3.10 Information Extraction
		1.3.11 Question Answering
		1.3.12 Opinion Mining and Sentiment Analysis
		1.3.13 Text Segmentation and Event Detection
	1.4 Summary
	1.5 Bibliographic Notes
		1.5.1 Software Resources
	1.6 Exercises
2 Text Preparation and Similarity Computation
	2.1 Introduction
	2.2 Raw Text Extraction and Tokenization
		2.2.1 Web-Specific Issues in Text Extraction
	2.3 Extracting Terms from Tokens
		2.3.1 Stop-Word Removal
		2.3.2 Hyphens
		2.3.3 Case Folding
		2.3.4 Usage-Based Consolidation
		2.3.5 Stemming
	2.4 Vector Space Representation and Normalization
	2.5 Similarity Computation in Text
		2.5.1 Is idf Normalization and Stemming Always Useful?
	2.6 Summary
	2.7 Bibliographic Notes
		2.7.1 Software Resources
	2.8 Exercises
3 Matrix Factorization and Topic Modeling
	3.1 Introduction
		3.1.1 Normalizing a Two-Way Factorization into a StandardizedThree-Way Factorization
	3.2 Singular Value Decomposition
		3.2.1 Example of SVD
		3.2.2 The Power Method of Implementing SVD
		3.2.3 Applications of SVD/LSA
		3.2.4 Advantages and Disadvantages of SVD/LSA
	3.3 Nonnegative Matrix Factorization
		3.3.1 Interpretability of Nonnegative Matrix Factorization
		3.3.2 Example of Nonnegative Matrix Factorization
		3.3.3 Folding in New Documents
		3.3.4 Advantages and Disadvantages of Nonnegative MatrixFactorization
	3.4 Probabilistic Latent Semantic Analysis
		3.4.1 Connections with Nonnegative Matrix Factorization
		3.4.2 Comparison with SVD
		3.4.3 Example of PLSA
		3.4.4 Advantages and Disadvantages of PLSA
	3.5 A Bird's Eye View of Latent Dirichlet Allocation
		3.5.1 Simplified LDA Model
		3.5.2 Smoothed LDA Model
	3.6 Nonlinear Transformations and Feature Engineering
		3.6.1 Choosing a Similarity Function
			3.6.1.1 Traditional Kernel Similarity Functions
			3.6.1.2 Generalizing Bag-of-Words to N-Grams
			3.6.1.3 String Subsequence Kernels
			3.6.1.4 Speeding Up the Recursion
			3.6.1.5 Language-Dependent Kernels
		3.6.2 Nyström Approximation
		3.6.3 Partial Availability of the Similarity Matrix
	3.7 Summary
	3.8 Bibliographic Notes
		3.8.1 Software Resources
	3.9 Exercises
4 Text Clustering
	4.1 Introduction
	4.2 Feature Selection and Engineering
		4.2.1 Feature Selection
			4.2.1.1 Term Strength
			4.2.1.2 Supervised Modeling for Unsupervised FeatureSelection
			4.2.1.3 Unsupervised Wrappers with Supervised FeatureSelection
		4.2.2 Feature Engineering
			4.2.2.1 Matrix Factorization Methods
			4.2.2.2 Nonlinear Dimensionality Reduction
	4.3 Topic Modeling and Matrix Factorization
		4.3.1 Mixed Membership Models and Overlapping Clusters
		4.3.2 Non-overlapping Clusters and Co-clustering: A MatrixFactorization View
			4.3.2.1 Co-clustering by Bipartite Graph Partitioning
	4.4 Generative Mixture Models for Clustering
		4.4.1 The Bernoulli Model
		4.4.2 The Multinomial Model
		4.4.3 Comparison with Mixed Membership Topic Models
		4.4.4 Connections with Naïve Bayes Model for Classification
	4.5 The k-Means Algorithm
		4.5.1 Convergence and Initialization
		4.5.2 Computational Complexity
		4.5.3 Connection with Probabilistic Models
	4.6 Hierarchical Clustering Algorithms
		4.6.1 Efficient Implementation and Computational Complexity
		4.6.2 The Natural Marriage with k-Means
	4.7 Clustering Ensembles
		4.7.1 Choosing the Ensemble Component
		4.7.2 Combining the Results from Different Components
	4.8 Clustering Text as Sequences
		4.8.1 Kernel Methods for Clustering
			4.8.1.1 Kernel k-Means
			4.8.1.2 Explicit Feature Engineering
			4.8.1.3 Kernel Trick or Explicit Feature Engineering?
		4.8.2 Data-Dependent Kernels: Spectral Clustering
	4.9 Transforming Clustering into Supervised Learning
	4.10 Clustering Evaluation
		4.10.1 The Pitfalls of Internal Validity Measures
		4.10.2 External Validity Measures
			4.10.2.1 Relationship of Clustering Evaluation to SupervisedLearning
			4.10.2.2 Common Mistakes in Evaluation
	4.11 Summary
	4.12 Bibliographic Notes
		4.12.1 Software Resources
	4.13 Exercises
5 Text Classification: Basic Models
	5.1 Introduction
		5.1.1 Types of Labels and Regression Modeling
		5.1.2 Training and Testing
		5.1.3 Inductive, Transductive, and Deductive Learners
		5.1.4 The Basic Models
		5.1.5 Text-Specific Challenges in Classifiers
	5.2 Feature Selection and Engineering
		5.2.1 Gini Index
		5.2.2 Conditional Entropy
		5.2.3 Pointwise Mutual Information
		5.2.4 Closely Related Measures
		5.2.5 The χ2-Statistic
		5.2.6 Embedded Feature Selection Models
		5.2.7 Feature Engineering Tricks
	5.3 The Naïve Bayes Model
		5.3.1 The Bernoulli Model
		5.3.2 Multinomial Model
		5.3.3 Practical Observations
		5.3.4 Ranking Outputs with Naïve Bayes
		5.3.5 Example of Naïve Bayes
			5.3.5.1 Bernoulli Model
			5.3.5.2 Multinomial Model
		5.3.6 Semi-Supervised Naïve Bayes
	5.4 Nearest Neighbor Classifier
		5.4.1 Properties of 1-Nearest Neighbor Classifiers
		5.4.2 Rocchio and Nearest Centroid Classification
		5.4.3 Weighted Nearest Neighbors
			5.4.3.1 Bagged and Subsampled 1-Nearest Neighbors as Weighted Nearest Neighbor Classifiers
		5.4.4 Adaptive Nearest Neighbors: A Powerful Family
	5.5 Decision Trees and Random Forests
		5.5.1 Basic Procedure for Decision Tree Construction
		5.5.2 Splitting a Node
		5.5.3 Multivariate Splits
		5.5.4 Problematic Issues with Decision Trees in Text Classification
		5.5.5 Random Forests
		5.5.6 Random Forests as Adaptive Nearest Neighbor Methods
	5.6 Rule-Based Classifiers
		5.6.1 Sequential Covering Algorithms
			5.6.1.1 Learn-One-Rule
		5.6.2 Generating Rules from Decision Trees
		5.6.3 Associative Classifiers
	5.7 Summary
	5.8 Bibliographic Notes
		5.8.1 Software Resources
	5.9 Exercises
6 Linear Models for Classification and Regression
	6.1 Introduction
		6.1.1 Geometric Interpretation of Linear Models
		6.1.2 Do We Need the Bias Variable?
		6.1.3 A General Definition of Linear Models with Regularization
		6.1.4 Generalizing Binary Predictions to Multiple Classes
		6.1.5 Characteristics of Linear Models for Text
	6.2 Least-Squares Regression and Classification
		6.2.1 Least-Squares Regression with L2-Regularization
			6.2.1.1 Efficient Implementation
			6.2.1.2 Approximate Estimation with Singular ValueDecomposition
			6.2.1.3 The Path to Kernel Regression
		6.2.2 LASSO: Least-Squares Regression with L1-Regularization
			6.2.2.1 Interpreting LASSO as a Feature Selector
		6.2.3 Fisher's Linear Discriminant and Least-Squares Classification
			6.2.3.1 Linear Discriminant with Multiple Classes
			6.2.3.2 Equivalence of Fisher Discriminant and Least-Squares Regression
			6.2.3.3 Regularized Least-Squares Classification and LLSF
			6.2.3.4 The Achilles Heel of Least-Squares Classification
	6.3 Support Vector Machines
		6.3.1 The Regularized Optimization Interpretation
		6.3.2 The Maximum Margin Interpretation
		6.3.3 Pegasos: Solving SVMs in the Primal
		6.3.4 Dual SVM Formulation
		6.3.5 Learning Algorithms for Dual SVMs
		6.3.6 Adaptive Nearest Neighbor Interpretation of Dual SVMs
	6.4 Logistic Regression
		6.4.1 The Regularized Optimization Interpretation
		6.4.2 Training Algorithms for Logistic Regression
		6.4.3 Probabilistic Interpretation of Logistic Regression
			6.4.3.1 Probabilistic Interpretation of Stochastic Gradient Descent Steps
			6.4.3.2 Relationships among Primal Updates of LinearModels
		6.4.4 Multinomial Logistic Regression and Other Generalizations
		6.4.5 Comments on the Performance of Logistic Regression
	6.5 Nonlinear Generalizations of Linear Models
		6.5.1 Kernel SVMs with Explicit Transformation
		6.5.2 Why do Conventional Kernels Promote Linear Separability?
		6.5.3 Strengths and Weaknesses of Different Kernels
		6.5.4 The Kernel Trick
		6.5.5 Systematic Application of the Kernel Trick
	6.6 Summary
	6.7 Bibliographic Notes
		6.7.1 Software Resources
	6.8 Exercises
7 Classifier Performance and Evaluation
	7.1 Introduction
	7.2 The Bias-Variance Trade-Off
		7.2.1 A Formal View
		7.2.2 Telltale Signs of Bias and Variance
	7.3 Implications of Bias-Variance Trade-Off on Performance
		7.3.1 Impact of Training Data Size
		7.3.2 Impact of Data Dimensionality
		7.3.3 Implications for Model Choice in Text
	7.4 Systematic Performance Enhancement with Ensembles
		7.4.1 Bagging and Subsampling
		7.4.2 Boosting
	7.5 Classifier Evaluation
		7.5.1 Segmenting into Training and Testing Portions
			7.5.1.1 Hold-Out
			7.5.1.2 Cross-Validation
		7.5.2 Absolute Accuracy Measures
			7.5.2.1 Accuracy of Classification
			7.5.2.2 Accuracy of Regression
		7.5.3 Ranking Measures for Classification and Information Retrieval
			7.5.3.1 Receiver Operating Characteristic
			7.5.3.2 Top-Heavy Measures for Ranked Lists
	7.6 Summary
	7.7 Bibliographic Notes
		7.7.1 Software Resources
		7.7.2 Data Sets for Evaluation
	7.8 Exercises
8 Joint Text Mining with Heterogeneous Data
	8.1 Introduction
	8.2 The Shared Matrix Factorization Trick
		8.2.1 The Factorization Graph
		8.2.2 Application: Shared Factorization with Text and Web Links
			8.2.2.1 Solving the Optimization Problem
			8.2.2.2 Supervised Embeddings
		8.2.3 Application: Text with Undirected Social Networks
			8.2.3.1 Application to Link Prediction with Text Content
		8.2.4 Application: Transfer Learning in Images with Text
			8.2.4.1 Transfer Learning with Unlabeled Text
			8.2.4.2 Transfer Learning with Labeled Text
		8.2.5 Application: Recommender Systems with Ratings and Text
		8.2.6 Application: Cross-Lingual Text Mining
	8.3 Factorization Machines
	8.4 Joint Probabilistic Modeling Techniques
		8.4.1 Joint Probabilistic Models for Clustering
		8.4.2 Naïve Bayes Classifier
	8.5 Transformation to Graph Mining Techniques
	8.6 Summary
	8.7 Bibliographic Notes
		8.7.1 Software Resources
	8.8 Exercises
9 Information Retrieval and Search Engines
	9.1 Introduction
	9.2 Indexing and Query Processing
		9.2.1 Dictionary Data Structures
		9.2.2 Inverted Index
		9.2.3 Linear Time Index Construction
		9.2.4 Query Processing
			9.2.4.1 Boolean Retrieval
			9.2.4.2 Ranked Retrieval
			9.2.4.3 Positional Queries
			9.2.4.4 Zoned Scoring
			9.2.4.5 Machine Learning in Information Retrieval
			9.2.4.6 Ranking Support Vector Machines
		9.2.5 Efficiency Optimizations
			9.2.5.1 Skip Pointers
			9.2.5.2 Champion Lists and Tiered Indexes
			9.2.5.3 Caching Tricks
			9.2.5.4 Compression Tricks
	9.3 Scoring with Information Retrieval Models
		9.3.1 Vector Space Models with tf-idf
		9.3.2 The Binary Independence Model
		9.3.3 The BM25 Model with Term Frequencies
		9.3.4 Statistical Language Models in Information Retrieval
			9.3.4.1 Query Likelihood Models
	9.4 Web Crawling and Resource Discovery
		9.4.1 A Basic Crawler Algorithm
		9.4.2 Preferential Crawlers
		9.4.3 Multiple Threads
		9.4.4 Combatting Spider Traps
		9.4.5 Shingling for Near Duplicate Detection
	9.5 Query Processing in Search Engines
		9.5.1 Distributed Index Construction
		9.5.2 Dynamic Index Updates
		9.5.3 Query Processing
		9.5.4 The Importance of Reputation
	9.6 Link-Based Ranking Algorithms
		9.6.1 PageRank
			9.6.1.1 Topic-Sensitive PageRank
			9.6.1.2 SimRank
		9.6.2 HITS
	9.7 Summary
	9.8 Bibliographic Notes
		9.8.1 Software Resources
	9.9 Exercises
10 Language Modeling and Deep Learning
	10.1 Introduction
	10.2 Statistical Language Models
		10.2.1 Skip-Gram Models
		10.2.2 Relationship with Embeddings
		10.2.3 Evaluating Language Models with Perplexity
	10.3 Kernel Methods for Sequence-Centric Learning
	10.4 Word-Context Matrix Factorization Models
		10.4.1 Matrix Factorization with Counts
		10.4.2 The GloVe Embedding
		10.4.3 PPMI Matrix Factorization
		10.4.4 Shifted PPMI Matrix Factorization
		10.4.5 Incorporating Syntactic and Other Features
	10.5 Graphical Representations of Word Distances
	10.6 Neural Networks and Word Embeddings
		10.6.1 Neural Networks: A Gentle Introduction
			10.6.1.1 Single Computational Layer: The Perceptron
			10.6.1.2 Multilayer Neural Networks
		10.6.2 Neural Embedding with Word2vec
			10.6.2.1 Neural Embedding with Continuous Bag of Words
			10.6.2.2 Neural Embedding with Skip-Gram Model
			10.6.2.3 Skip-Gram with Negative Sampling
			10.6.2.4 What Is the Actual Neural Architecture of SGNS?
		10.6.3 Word2vec (SGNS) Is Logistic Matrix Factorization
		10.6.4 Beyond Words: Embedding Paragraphs with Doc2vec
	10.7 Recurrent Neural Networks
		10.7.1 Language Modeling Example of RNN
			10.7.1.1 Generating a Language Sample
		10.7.2 Backpropagation Through Time
		10.7.3 Bidirectional Recurrent Networks
		10.7.4 Multilayer Recurrent Networks
		10.7.5 Long Short-Term Memory (LSTM)
		10.7.6 Gated Recurrent Units (GRUs)
		10.7.7 Layer Normalization
	10.8 Applications of Recurrent Neural Networks
		10.8.1 Contextual Word Embeddings with ELMo
		10.8.2 Application to Automatic Image Captioning
		10.8.3 Sequence-to-Sequence Learning and Machine Translation
			10.8.3.1 BLEU Score for Evaluating Machine Translation
		10.8.4 Application to Sentence-Level Classification
		10.8.5 Token-Level Classification with Linguistic Features
	10.9 Convolutional Neural Networks for Text
	10.10 Summary
	10.11 Bibliographic Notes
		10.11.1 Software Resources
	10.12 Exercises
11 Attention Mechanisms and Transformers
	11.1 Introduction
	11.2 Attention Mechanisms for Machine Translation
		11.2.1 The Luong Attention Model
		11.2.2 Variations and Comparison with Bahdanau Attention
	11.3 Transformer Networks
		11.3.1 How Self Attention Helps
		11.3.2 The Self-Attention Module
		11.3.3 Incorporating Positional Information
		11.3.4 The Sequence-to-Sequence Transformer
		11.3.5 Multihead Attention
	11.4 Transformer-Based Pre-trained Language Models
		11.4.1 GPT-n
		11.4.2 BERT
		11.4.3 T5
	11.5 Natural Language Processing Applications
		11.5.1 The GLUE and SuperGLUE Benchmarks
		11.5.2 The Corpus of Linguistic Acceptability (CoLA)
		11.5.3 Sentiment Analysis
		11.5.4 Token-Level Classification
		11.5.5 Machine Translation and Summarization
		11.5.6 Textual Entailment
		11.5.7 Semantic Textual Similarity
		11.5.8 Word Sense Disambiguation
		11.5.9 Co-Reference Resolution
		11.5.10 Question Answering
	11.6 Summary
	11.7 Bibliographic Notes
		11.7.1 Software Resources
	11.8 Exercises
12 Text Summarization
	12.1 Introduction
		12.1.1 Extractive and Abstractive Summarization
		12.1.2 Key Steps in Extractive Summarization
		12.1.3 The Segmentation Phase in Extractive Summarization
	12.2 Topic Word Methods for Extractive Summarization
		12.2.1 Word Probabilities
		12.2.2 Normalized Frequency Weights
		12.2.3 Topic Signatures
		12.2.4 Sentence Selection Methods
	12.3 Latent Methods for Extractive Summarization
		12.3.1 Latent Semantic Analysis
		12.3.2 Lexical Chains
			12.3.2.1 Short Description of WordNet
			12.3.2.2 Leveraging WordNet for Lexical Chains
		12.3.3 Graph-Based Methods
		12.3.4 Centroid Summarization
	12.4 Traditional Machine Learning for Extractive Summarization
		12.4.1 Feature Extraction
		12.4.2 Which Classifiers to Use?
	12.5 Deep Learning for Extractive Summarization
		12.5.1 Recurrent Neural Networks
		12.5.2 Using Pre-Trained Language Models with Transformers
	12.6 Multi-Document Summarization
		12.6.1 Centroid-Based Summarization
		12.6.2 Graph-Based Methods
	12.7 Abstractive Summarization
		12.7.1 Sentence Compression
		12.7.2 Information Fusion
		12.7.3 Information Ordering
		12.7.4 Recurrent Neural Networks for Summarization
		12.7.5 Abstractive Summarization with Transformers
	12.8 Summary
	12.9 Bibliographic Notes
		12.9.1 Software Resources
	12.10 Exercises
13 Information Extraction and Knowledge Graphs
	13.1 Introduction
		13.1.1 Historical Evolution
		13.1.2 The Role of Natural Language Processing
	13.2 Named Entity Recognition
		13.2.1 Rule-Based Methods
			13.2.1.1 Training Algorithms for Rule-Based Systems
		13.2.2 Transformation to Token-Level Classification
		13.2.3 Hidden Markov Models
			13.2.3.1 Training
			13.2.3.2 Prediction for Test Segment
			13.2.3.3 Incorporating Extracted Features
			13.2.3.4 Variations and Enhancements
		13.2.4 Maximum Entropy Markov Models
		13.2.5 Conditional Random Fields
		13.2.6 Deep Learning for Entity Extraction
			13.2.6.1 Recurrent Neural Networks for Named EntityRecognition
			13.2.6.2 Use of Pretrained Language Modelswith Transformers
	13.3 Relationship Extraction
		13.3.1 Transformation to Classification
		13.3.2 Relationship Prediction with Explicit Feature Engineering
		13.3.3 Relationship Prediction with Implicit Feature Engineering: Kernel Methods
			13.3.3.1 Kernels from Dependency Graphs
			13.3.3.2 Subsequence-Based Kernels
			13.3.3.3 Convolution Tree-Based Kernels
		13.3.4 Relationship Extraction with Pretrained Language Models
	13.4 Knowledge Graphs
		13.4.1 Constructing a Knowledge Graph
		13.4.2 Knowledge Graphs in Search
	13.5 Summary
	13.6 Bibliographic Notes
		13.6.1 Weakly Supervised Learning Methods
		13.6.2 Unsupervised and Open Information Extraction
		13.6.3 Software Resources
	13.7 Exercises
14 Question Answering
	14.1 Introduction
	14.2 The Reading Comprehension Task
		14.2.1 Using Recurrent Neural Networks with Attention
		14.2.2 Leveraging Pretrained Language Models
	14.3 Retrieval for Open-Domain Question Answering
		14.3.1 Dense Retrieval in Open Retriever Question Answering
		14.3.2 Salient Span Masking
	14.4 Closed Book Systems with Pretrained Language Models
	14.5 Question Answering with Knowledge Graphs
		14.5.1 Leveraging Query Translation
		14.5.2 Fusing Text and Structured Data
		14.5.3 Knowledge Graph to Corpus Translation
	14.6 Challenges of Long-Form Question Answering
	14.7 Summary
	14.8 Bibliographic Notes
		14.8.1 Data Sets for Evaluation
		14.8.2 Software Resources
	14.9 Exercises
15 Opinion Mining and Sentiment Analysis
	15.1 Introduction
		15.1.1 The Opinion Lexicon
	15.2 Document-Level Sentiment Classification
		15.2.1 Unsupervised Approaches to Classification
	15.3 Phrase- and Sentence-Level Sentiment Classification
		15.3.1 Applications of Sentence- and Phrase-Level Analysis
		15.3.2 Reduction of Subjectivity Classification to Minimum CutProblem
		15.3.3 Context in Sentence- and Phrase-Level Polarity Analysis
		15.3.4 Sentiment Analysis with Deep Learning
			15.3.4.1 Recurrent Neural Networks
			15.3.4.2 Leveraging Pretrained Language Modelswith Transformers
	15.4 Aspect-Based Opinion Mining as Information Extraction
		15.4.1 Hu and Liu's Unsupervised Approach
		15.4.2 OPINE: An Unsupervised Approach
		15.4.3 Supervised Opinion Extraction as Token-Level Classification
	15.5 Opinion Spam
		15.5.1 Supervised Methods for Spam Detection
			15.5.1.1 Labeling Deceptive Spam
			15.5.1.2 Feature Extraction
		15.5.2 Unsupervised Methods for Spammer Detection
	15.6 Opinion Summarization
	15.7 Summary
	15.8 Bibliographic Notes
		15.8.1 Software Resources
	15.9 Exercises
16 Text Segmentation and Event Detection
	16.1 Introduction
		16.1.1 Relationship with Topic Detection and Tracking
	16.2 Text Segmentation
		16.2.1 TextTiling
		16.2.2 The C99 Approach
		16.2.3 Supervised Segmentation with Off-the-Shelf Classifiers
		16.2.4 Supervised Segmentation with Markovian Models
	16.3 Mining Text Streams
		16.3.1 Streaming Text Clustering
		16.3.2 Application to First Story Detection
	16.4 Event Detection
		16.4.1 Unsupervised Event Detection
			16.4.1.1 Window-Based Nearest-Neighbor Method
			16.4.1.2 Leveraging Generative Models
			16.4.1.3 Event Detection in Social Streams
		16.4.2 Supervised Event Detection as Supervised Segmentation
		16.4.3 Event Detection as an Information Extraction Problem
			16.4.3.1 Transformation to Token-Level Classification
			16.4.3.2 Open Domain Event Extraction
	16.5 Summary
	16.6 Bibliographic Notes
		16.6.1 Software Resources
	16.7 Exercises
Index