StarNek0/slp

📖 [译] 《自然语言处理综论(Speech and Language Processing)》第三版

slp

📖 [译] 《自然语言处理综论(Speech and Language Processing)》第三版

书名	Speech and Language Processing
作者	Dan Jurafsky and James H. Martin
版本	3rd ed. draft
日期	2020-12-30
官网	https://web.stanford.edu/~jurafsky/slp3
PDF	https://web.stanford.edu/~jurafsky/slp3/ed3book_dec302020.pdf

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章	节	翻译	进度	校对
1 Introduction	-	StarDrewer	100%
2 Regular Expressions, Text Normalization, Edit Distance	-	StarDrewer	40%
	2.1 Regular Expressions
	2.2 Words
	2.3 Corpora
	2.4 Text Normalization
	2.5 Minimum Edit Distance
	2.6 Summary
	Bibliographical and Historical Notes
	Exercises
3 N-gram Language Models
	3.1 N-Grams
	3.2 Evaluating Language Models
	3.3 Generalization and Zeros
	3.4 Smoothing
	3.5 Kneser-Ney Smoothing
	3.6 Huge Language Models and Stupid Backoff
	3.7 Advanced: Perplexity’s Relation to Entropy
	3.8 Summary
	Bibliographical and Historical Notes
	Exercises
4 Naive Bayes and Sentiment Classification
	4.1 Naive Bayes Classifiers
	4.2 Training the Naive Bayes Classifier
	4.3 Worked example
	4.4 Optimizing for Sentiment Analysis
	4.5 Naive Bayes for other text classification tasks
	4.6 Naive Bayes as
	4.7 Evaluation: Precision, Recall, F-measure
	4.8 Test sets and Cross-validation
	4.9 Statistical Significance Testing
	4.10 Avoiding Harms in Classification
	4.11 Summary
	Bibliographical and Historical Notes
	Exercises
5 Logistic Regression
	5.1 Classification: the sigmoid
	5.2 Learning in Logistic Regression
	5.3 The cross-entropy loss function
	5.4 Gradient Descent
	5.5 Regularization
	5.6 Multinomial logistic regression
	5.7 Interpreting models
	5.8 Advanced: Deriving the Gradient Equation
	5.9 Summary
	Bibliographical and Historical Notes
	Exercises
6 Vector Semantics and Embeddings
	6.1 Lexical Semantics
	6.2 Vector Semantics
	6.3 Words and Vectors
	6.4 Cosine for measuring similarity
	6.5 TF-IDF: Weighing terms in the vector
	6.6 Pointwise Mutual Information (PMI)
	6.7 Applications of the tf-idf or PPMI vector models
	6.8 Word2vec
	6.9 Visualizing Embeddings
	6.10 Semantic properties of embeddings
	6.11 Bias and Embeddings
	6.12 Evaluating Vector Models
	6.13 Summary
	Bibliographical and Historical Notes
	Exercises
7 Neural Networks and Neural Language Models
	7.1 Units
	7.2 The XOR problem
	7.3 Feed-Forward Neural Networks
	7.4 Training Neural Nets
	7.5 Neural Language Models
	7.6 Summary
	Bibliographical and Historical Notes
8 Sequence Labeling for Parts of Speech and Named Entities
	8.1 (Mostly) English Word Classes
	8.2 Part-of-Speech Tagging
	8.3 Named Entities and Named Entity Tagging
	8.4 HMM Part-of-Speech Tagging
	8.5 Conditional Random Fields (CRFs)
	8.6 Evaluation of Named Entity Recognition
	8.7 Further Details
	8.8 Summary
	Bibliographical and Historical Notes
	Exercises
9 Deep Learning Architectures for Sequence Processing
	9.1 Language Models Revisited
	9.2 Recurrent Neural Networks
	9.3 Managing Context in RNNs: LSTMs and GRUs
	9.4 Self-Attention Networks: Transformers
	9.5 Potential Harms from Language Models
	9.6 Summary
	Bibliographical and Historical Notes
10 Contextual Embeddings
11 Machine Translation and Encoder-Decoder Models
	11.1 Language Divergences and Typology
	11.2 The Encoder-Decoder Model
	11.3 Encoder-Decoder with RNNs
	11.4 Attention
	11.5 Beam Search
	11.6 Encoder-Decoder with Transformers
	11.7 Some practical details on building MT systems
	11.8 MT Evaluation
	11.9 Bias and Ethical Issues
	11.10 Summary
	Bibliographical and Historical Notes
	Exercises
12 Constituency Grammars
	12.1 Constituency
	12.2 Context-Free Grammars
	12.3 Some Grammar Rules for English
	12.4 Treebanks
	12.5 Grammar Equivalence and Normal Form
	12.6 Lexicalized Grammars
	12.7 Summary
	Bibliographical and Historical Notes
	Exercises
13 Constituency Parsing
	13.1 Ambiguity
	13.2 CKY Parsing:
	13.3 Span-Based Neural Constituency Parsing
	13.4 Evaluating Parsers
	13.5 Partial Parsing
	13.6 CCG Parsing
	13.7 Summary
	Bibliographical and Historical Notes
	Exercises
14 Dependency Parsing
	14.1 Dependency Relations
	14.2 Dependency Formalisms
	14.3 Dependency Treebanks
	14.4 Transition-Based Dependency Parsing
	14.5 Graph-Based Dependency Parsing
	14.6 Evaluation
	14.7 Summary
	Bibliographical and Historical Notes
	Exercises
15 Logical Representations of Sentence Meaning
	15.1 Computational Desiderata for Representations
	15.2 Model-Theoretic Semantics
	15.3 First-Order Logic
	15.4 Event and State Representations
	15.5 Description Logics
	15.6 Summary
	Bibliographical and Historical Notes
	Exercises
16 Computational Semantics and Semantic Parsing
17 Information Extraction
	17.1 Relation Extraction
	17.2 Relation Extraction Algorithms
	17.3 Extracting Times
	17.4 Extracting Events and their Times
	17.5 Template Filling
	17.6 Summary
	Bibliographical and Historical Notes
	Exercises
18 Word Senses and WordNet
	18.1 Word Senses
	18.2 Relations Between Senses
	18.3 WordNet:
	18.4 Word Sense Disambiguation
	18.5 Alternate WSD algorithms and Tasks
	18.6 Using Thesauruses to Improve Embeddings
	18.7 Word Sense Induction
	18.8 Summary
	Bibliographical and Historical Notes
	Exercises
19 Semantic Role Labeling
	19.1 Semantic Roles
	19.2 Diathesis Alternations
	19.3 Semantic Roles: Problems with Thematic Roles
	19.4 The Proposition Bank
	19.5 FrameNet
	19.6 Semantic Role Labeling
	19.7 Selectional Restrictions
	19.8 Primitive Decomposition of Predicates
	19.9 Summary
	Bibliographical and Historical Notes
	Exercises
20 Lexicons for Sentiment, Affect, and Connotation
	20.1 Defining Emotion
	20.2 Available Sentiment and Affect Lexicons
	20.3 Creating Affect Lexicons by Human Labeling
	20.4 Semi-supervised Induction of Affect Lexicons
	20.5 Supervised Learning of Word Sentiment
	20.6 Using Lexicons for Sentiment Recognition
	20.7 Other tasks: Personality
	20.8 Affect Recognition
	20.9 Lexicon-based methods for Entity-Centric Affect
	20.10 Connotation Frames
	20.11 Summary
	Bibliographical and Historical Notes
21 Coreference Resolution
	21.1 Coreference Phenomena: Linguistic Background
	21.2 Coreference Tasks and Datasets
	21.3 Mention Detection
	21.4 Architectures for Coreference Algorithms
	21.5 Classifiers using hand-built features
	21.6
	21.7 Evaluation of Coreference Resolution
	21.8 Winograd Schema problems
	21.9 Gender Bias in Coreference
	21.10 Summary
	Bibliographical and Historical Notes
	Exercises
22 Discourse Coherence
	22.1 Coherence Relations
	22.2 Discourse Structure Parsing
	22.3 Centering and Entity-Based Coherence
	22.4 Representation learning models for local coherence
	22.5 Global Coherence
	22.6 Summary
	Bibliographical and Historical Notes
	Exercises
23 Question Answering
	23.1 Information Retrieval
	23.2 IR-based Factoid Question Answering
	23.3 Entity Linking
	23.4 Knowledge-based Question Answering
	23.5 Using Language Models to do QA
	23.6 Classic QA Models
	23.7 Evaluation of Factoid Answers
	Bibliographical and Historical Notes
	Exercises
24 Chatbots
	24.1 Properties of Human Conversation
	24.2 Chatbots
	24.3 GUS: Simple Frame-based Dialogue Systems
	24.4 The Dialogue-State Architecture
	24.5 Evaluating Dialogue Systems
	24.6 Dialogue System Design
	24.7 Summary
	Bibliographical and Historical Notes
	Exercises
25 Phonetics
	25.1 Speech Sounds and Phonetic Transcription
	25.2 Articulatory Phonetics
	25.3 Prosody
	25.4 Acoustic Phonetics and Signals
	25.5 Phonetic Resources
	25.6 Summary
	Bibliographical and Historical Notes
	Exercises
26 Automatic Speech Recognition and Text-to-Speech
	26.1 The Automatic Speech Recognition Task
	26.2 Feature Extraction for ASR: Log Mel Spectrum
	26.3 Speech Recognition Architecture
	26.4 CTC
	26.5 ASR Evaluation: Word Error Rate
	26.6 TTS
	26.7 Other Speech Tasks
	26.8 Summary
	Bibliographical and Historical Notes
	Exercises
Bibliography
Subject Index