Deep Learning for Natural Language Processing

Deep Learning for NLP
brief contents
contents
preface
acknowledgments
about this book
	Who should read this book
	How this book is organized: A road map
	About the code
	liveBook discussion forum
about the author
about the cover illustration
Part 1 Introduction
	1 Deep learning for NLP
		1.1 A selection of machine learning methods for NLP
			1.1.1 The perceptron
			1.1.2 Support vector machines
			1.1.3 Memory-based learning
		1.2 Deep learning
		1.3 Vector representations of language
			1.3.1 Representational vectors
			1.3.2 Operational vectors
		1.4 Vector sanitization
			1.4.1 The hashing trick
			1.4.2 Vector normalization
		Summary
	2 Deep learning and language: The basics
		2.1 Basic architectures of deep learning
			2.1.1 Deep multilayer perceptrons
			2.1.2 Two basic operators: Spatial and temporal
		2.2 Deep learning and NLP: A new paradigm
		Summary
	3 Text embeddings
		3.1 Embeddings
			3.1.1 Embedding by direct computation: Representational embeddings
			3.1.2 Learning to embed: Procedural embeddings
		3.2 From words to vectors: Word2Vec
		3.3 From documents to vectors: Doc2Vec
		Summary
Part 2 Deep NLP
	4 Textual similarity
		4.1 The problem
		4.2 The data
			4.2.1 Authorship attribution and verification data
		4.3 Data representation
			4.3.1 Segmenting documents
			4.3.2 Word-level information
			4.3.3 Subword-level information
		4.4 Models for measuring similarity
			4.4.1 Authorship attribution
			4.4.2 Verifying authorship
		Summary
	5 Sequential NLP
		5.1 Memory and language
			5.1.1 The problem: Question Answering
		5.2 Data and data processing
		5.3 Question Answering with sequential models
			5.3.1 RNNs for Question Answering
			5.3.2 LSTMs for Question Answering
			5.3.3 End-to-end memory networks for Question Answering
		Summary
	6 Episodic memory for NLP
		6.1 Memory networks for sequential NLP
		6.2 Data and data processing
			6.2.1 PP-attachment data
			6.2.2 Dutch diminutive data
			6.2.3 Spanish part-of-speech data
		6.3 Strongly supervised memory networks: Experiments and results
			6.3.1 PP-attachment
			6.3.2 Dutch diminutives
			6.3.3 Spanish part-of-speech tagging
		6.4 Semi-supervised memory networks
			6.4.1 Semi-supervised memory networks: Experiments and results
		Summary
Part 3 Advanced topics
	7 Attention
		7.1 Neural attention
		7.2 Data
		7.3 Static attention: MLP
		7.4 Temporal attention: LSTM
		7.5 Experiments
			7.5.1 MLP
			7.5.2 LSTM
		Summary
	8 Multitask learning
		8.1 Introduction to multitask learning
		8.2 Multitask learning
		8.3 Multitask learning for consumer reviews: Yelp and Amazon
			8.3.1 Data handling
			8.3.2 Hard parameter sharing
			8.3.3 Soft parameter sharing
			8.3.4 Mixed parameter sharing
		8.4 Multitask learning for Reuters topic classification
			8.4.1 Data handling
			8.4.2 Hard parameter sharing
			8.4.3 Soft parameter sharing
			8.4.4 Mixed parameter sharing
		8.5 Multitask learning for part-of-speech tagging and named-entity recognition
			8.5.1 Data handling
			8.5.2 Hard parameter sharing
			8.5.3 Soft parameter sharing
			8.5.4 Mixed parameter sharing
		Summary
	9 Transformers
		9.1 BERT up close: Transformers
		9.2 Transformer encoders
			9.2.1 Positional encoding
		9.3 Transformer decoders
		9.4 BERT: Masked language modeling
			9.4.1 Training BERT
			9.4.2 Fine-tuning BERT
			9.4.3 Beyond BERT
		Summary
	10 Applications of Transformers: Hands-on with BERT
		10.1 Introduction: Working with BERT in practice
		10.2 A BERT layer
		10.3 Training BERT on your data
		10.4 Fine-tuning BERT
		10.5 Inspecting BERT
			10.5.1 Homonyms in BERT
		10.6 Applying BERT
		Summary
bibliography
index
	Numerics
	A
	B
	C
	D
	E
	F
	G
	H
	I
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	Y
	Z
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