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jurafsky d martin j speech language processing 3ed 2023

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Textbook in PDF format Fundamental Algorithms for NLP Introduction Regular Expressions, Text Normalization, Edit Distance Regular Expressions Basic Regular Expression Patterns Disjunction, Grouping, and Precedence A Simple Example More Operators A More Complex Example Substitution, Capture Groups, and ELIZA Lookahead Assertions Words Corpora Text Normalization Unix Tools for Crude Tokenization and Normalization Word Tokenization Byte-Pair Encoding for Tokenization Word Normalization, Lemmatization and Stemming Sentence Segmentation Minimum Edit Distance The Minimum Edit Distance Algorithm Summary Bibliographical and Historical Notes Exercises N-gram Language Models N-Grams Evaluating Language Models Perplexity Sampling sentences from a language model Generalization and Zeros Unknown Words Smoothing Laplace Smoothing Add-k smoothing Backoff and Interpolation Huge Language Models and Stupid Backoff Advanced: Kneser-Ney Smoothing Absolute Discounting Kneser-Ney Discounting Advanced: Perplexity's Relation to Entropy Summary Bibliographical and Historical Notes Exercises Naive Bayes, Text Classification, and Sentiment Naive Bayes Classifiers Training the Naive Bayes Classifier Worked example Optimizing for Sentiment Analysis Naive Bayes for other text classification tasks Naive Bayes as a Language Model Evaluation: Precision, Recall, F-measure Evaluating with more than two classes Test sets and Cross-validation Statistical Significance Testing The Paired Bootstrap Test Avoiding Harms in Classification Summary Bibliographical and Historical Notes Exercises Logistic Regression The sigmoid function Classification with Logistic Regression Sentiment Classification Other classification tasks and features Processing many examples at once Choosing a classifier Multinomial logistic regression Softmax Applying softmax in logistic regression Features in Multinomial Logistic Regression Learning in Logistic Regression The cross-entropy loss function Gradient Descent The Gradient for Logistic Regression The Stochastic Gradient Descent Algorithm Working through an example Mini-batch training Regularization Learning in Multinomial Logistic Regression Interpreting models Advanced: Deriving the Gradient Equation Summary Bibliographical and Historical Notes Exercises Vector Semantics and Embeddings Lexical Semantics Vector Semantics Words and Vectors Vectors and documents Words as vectors: document dimensions Words as vectors: word dimensions Cosine for measuring similarity TF-IDF: Weighing terms in the vector Pointwise Mutual Information (PMI) Applications of the tf-idf or PPMI vector models Word2vec The classifier Learning skip-gram embeddings Other kinds of static embeddings Visualizing Embeddings Semantic properties of embeddings Embeddings and Historical Semantics Bias and Embeddings Evaluating Vector Models Summary Bibliographical and Historical Notes Exercises Neural Networks and Neural Language Models Units The XOR problem The solution: neural networks Feedforward Neural Networks More details on feedforward networks Feedforward networks for NLP: Classification Feedforward Neural Language Modeling Forward inference in the neural language model Training Neural Nets Loss function Computing the Gradient Computation Graphs Backward differentiation on computation graphs More details on learning Training the neural language model Summary Bibliographical and Historical Notes Sequence Labeling for Parts of Speech and Named Entities (Mostly) English Word Classes Part-of-Speech Tagging Named Entities and Named Entity Tagging HMM Part-of-Speech Tagging Markov Chains The Hidden Markov Model The components of an HMM tagger HMM tagging as decoding The Viterbi Algorithm Working through an example Conditional Random Fields (CRFs) Features in a CRF POS Tagger Features for CRF Named Entity Recognizers Inference and Training for CRFs Evaluation of Named Entity Recognition Further Details Rule-based Methods POS Tagging for Morphologically Rich Languages Summary Bibliographical and Historical Notes Exercises RNNs and LSTMs Recurrent Neural Networks Inference in RNNs Training RNNs as Language Models Forward Inference in an RNN language model Training an RNN language model Weight Tying RNNs for other NLP tasks Sequence Labeling RNNs for Sequence Classification Generation with RNN-Based Language Models Stacked and Bidirectional RNN architectures Stacked RNNs Bidirectional RNNs The LSTM Gated Units, Layers and Networks Summary: Common RNN NLP Architectures The Encoder-Decoder Model with RNNs Training the Encoder-Decoder Model Attention Summary Bibliographical and Historical Notes Transformers and Pretrained Language Models Self-Attention Networks: Transformers Transformer Blocks Multihead Attention Modeling word order: positional embeddings Transformers as Language Models Sampling Beam Search Pretraining Large Language Models Language Models for Zero-shot Learning Potential Harms from Language Models Summary Bibliographical and Historical Notes Fine-Tuning and Masked Language Models Bidirectional Transformer Encoders Training Bidirectional Encoders Masking Words Masking Spans Next Sentence Prediction Training Regimes Contextual Embeddings Transfer Learning through Fine-Tuning Sequence Classification Pair-Wise Sequence Classification Sequence Labelling Fine-tuning for Span-Based Applications Training Corpora Summary Bibliographical and Historical Notes Prompting, In-Context Learning, and Instruct Tuning II NLP Applications Machine Translation Language Divergences and Typology Word Order Typology Lexical Divergences Morphological Typology Referential density Machine Translation using Encoder-Decoder Tokenization Creating the Training data Details of the Encoder-Decoder Model Translating in low-resource situations Data Augmentation Multilingual models Sociotechnical issues MT Evaluation Using Human Raters to Evaluate MT Automatic Evaluation Automatic Evaluation: Embedding-Based Methods Bias and Ethical Issues Summary Bibliographical and Historical Notes Exercises Question Answering and Information Retrieval Information Retrieval Term weighting and document scoring Document Scoring Inverted Index Evaluation of Information-Retrieval Systems IR with Dense Vectors IR-based Factoid Question Answering IR-based QA: Datasets IR-based QA: Reader (Answer Span Extraction) Entity Linking Linking based on Anchor Dictionaries and Web Graph Neural Graph-based linking Knowledge-based Question Answering Knowledge-Based QA from RDF triple stores QA by Semantic Parsing Using Language Models to do QA Classic QA Models Evaluation of Factoid Answers Bibliographical and Historical Notes Exercises Chatbots & Dialogue Systems Properties of Human Conversation Chatbots Rule-based chatbots: ELIZA and PARRY Corpus-based chatbots Hybrid architectures GUS: Simple Frame-based Dialogue Systems Control structure for frame-based dialogue Determining Domain, Intent, and Slot fillers in GUS Other components of frame-based dialogue The Dialogue-State Architecture Dialogue Acts Slot Filling Dialogue State Tracking Dialogue Policy Natural language generation in the dialogue-state model Evaluating Dialogue Systems Evaluating Chatbots Evaluating Task-Based Dialogue Dialogue System Design Ethical Issues in Dialogue System Design Summary Bibliographical and Historical Notes Exercises Automatic Speech Recognition and Text-to-Speech The Automatic Speech Recognition Task Feature Extraction for ASR: Log Mel Spectrum Sampling and Quantization Windowing Discrete Fourier Transform Mel Filter Bank and Log Speech Recognition Architecture Learning CTC CTC Inference CTC Training Combining CTC and Encoder-Decoder Streaming Models: RNN-T for improving CTC ASR Evaluation: Word Error Rate TTS TTS Preprocessing: Text normalization TTS: Spectrogram prediction TTS: Vocoding TTS Evaluation Other Speech Tasks Summary Bibliographical and Historical Notes Exercises III Annotating Linguistic Structure Context-Free Grammars and Constituency Parsing Constituency Context-Free Grammars Formal Definition of Context-Free Grammar Treebanks Grammar Equivalence and Normal Form Ambiguity CKY Parsing: A Dynamic Programming Approach Conversion to Chomsky Normal Form CKY Recognition CKY Parsing CKY in Practice Span-Based Neural Constituency Parsing Computing Scores for a Span Integrating Span Scores into a Parse Evaluating Parsers Heads and Head-Finding Summary Bibliographical and Historical Notes Exercises Dependency Parsing Dependency Relations Dependency Formalisms Projectivity Dependency Treebanks Transition-Based Dependency Parsing Creating an Oracle A feature-based classifier A neural classifier Advanced Methods in Transition-Based Parsing Graph-Based Dependency Parsing Parsing via finding the maximum spanning tree A feature-based algorithm for assigning scores A neural algorithm for assigning scores Evaluation Summary Bibliographical and Historical Notes Exercises Logical Representations of Sentence Meaning Computational Desiderata for Representations Model-Theoretic Semantics First-Order Logic Basic Elements of First-Order Logic Variables and Quantifiers Lambda Notation The Semantics of First-Order Logic Inference Event and State Representations Description Logics Summary Bibliographical and Historical Notes Exercises Computational Semantics and Semantic Parsing Relation and Event Extraction Relation Extraction Relation Extraction Algorithms Using Patterns to Extract Relations Relation Extraction via Supervised Learning Semisupervised Relation Extraction via Bootstrapping Distant Supervision for Relation Extraction Unsupervised Relation Extraction Evaluation of Relation Extraction Extracting Events Template Filling Machine Learning Approaches to Template Filling Earlier Finite-State Template-Filling Systems Summary Bibliographical and Historical Notes Exercises Time and Temporal Reasoning Representing Time Reichenbach's reference point Representing Aspect Temporally Annotated Datasets: TimeBank Automatic Temporal Analysis Extracting Temporal Expressions Temporal Normalization Temporal Ordering of Events Summary Bibliographical and Historical Notes Exercises Word Senses and WordNet Word Senses Defining Word Senses How many senses do words have? Relations Between Senses WordNet: A Database of Lexical Relations Sense Relations in WordNet Word Sense Disambiguation WSD: The Task and Datasets The WSD Algorithm: Contextual Embeddings Alternate WSD algorithms and Tasks Feature-Based WSD The Lesk Algorithm as WSD Baseline Word-in-Context Evaluation Wikipedia as a source of training data Using Thesauruses to Improve Embeddings Word Sense Induction Summary Bibliographical and Historical Notes Exercises Semantic Role Labeling Semantic Roles Diathesis Alternations Semantic Roles: Problems with Thematic Roles The Proposition Bank FrameNet Semantic Role Labeling A Feature-based Algorithm for Semantic Role Labeling A Neural Algorithm for Semantic Role Labeling Evaluation of Semantic Role Labeling Selectional Restrictions Representing Selectional Restrictions Selectional Preferences Primitive Decomposition of Predicates Summary Bibliographical and Historical Notes Exercises Lexicons for Sentiment, Affect, and Connotation Defining Emotion Available Sentiment and Affect Lexicons Creating Affect Lexicons by Human Labeling Semi-supervised Induction of Affect Lexicons Semantic Axis Methods Label Propagation Other Methods Supervised Learning of Word Sentiment Log Odds Ratio Informative Dirichlet Prior Using Lexicons for Sentiment Recognition Using Lexicons for Affect Recognition Lexicon-based methods for Entity-Centric Affect Connotation Frames Summary Bibliographical and Historical Notes Exercises Coreference Resolution Coreference Phenomena: Linguistic Background Types of Referring Expressions Information Status Complications: Non-Referring Expressions Linguistic Properties of the Coreference Relation Coreference Tasks and Datasets Mention Detection Architectures for Coreference Algorithms The Mention-Pair Architecture The Mention-Rank Architecture Entity-based Models Classifiers using hand-built features A neural mention-ranking algorithm Computing span representations Computing the mention and antecedent scores m and c Learning Evaluation of Coreference Resolution Winograd Schema problems Gender Bias in Coreference Summary Bibliographical and Historical Notes Exercises Discourse Coherence Coherence Relations Rhetorical Structure Theory Penn Discourse TreeBank (PDTB) Discourse Structure Parsing EDU segmentation for RST parsing RST parsing PDTB discourse parsing Centering and Entity-Based Coherence Centering Entity Grid model Evaluating Neural and Entity-based coherence Representation learning models for local coherence Global Coherence Argumentation Structure The structure of scientific discourse Summary Bibliographical and Historical Notes Exercises Phonetics Speech Sounds and Phonetic Transcription Articulatory Phonetics Prosody Prosodic Prominence: Accent, Stress and Schwa Prosodic Structure Tune Acoustic Phonetics and Signals Waves Speech Sound Waves Frequency and Amplitude; Pitch and Loudness Interpretation of Phones from a Waveform Spectra and the Frequency Domain The Source-Filter Model Phonetic Resources Summary Bibliographical and Historical Notes Exercises Bibliography Subject Index

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jurafsky d martin j speech language processing 3ed 2023

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