Deep learning for natural language processing / Stephan Raaijmakers.

Format:

Book

Author/Creator:

Raaijmakers, Stephan, author.

Language:

English

Subjects (All):

Natural language processing (Computer science).

Speech processing systems.

Machine learning.

Physical Description:

1 online resource (242 pages)

Edition:

[First edition].

Place of Publication:

Shelter Island, New York : Manning Publications Co. LLC, [2022]

Summary:

Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses!

Contents:

Intro

Deep Learning for Natural Language Processing

Copyright

brief contents

contents

front matter

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

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

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

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

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

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

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

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

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

bibliography

index.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781638353997

1638353999

OCLC:

1352970516