Deep Learning for Natural Language Processing
Stephan Raaijmakers
  • MEAP began January 2019
  • Publication in Spring 2021 (estimated)
  • ISBN 9781617295447
  • 292 pages (estimated)
  • printed in black & white

Provides comprehensive treatment of the subject and will provide the reader with accurate, timely information.

Philippe Van Bergen
Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Through detailed instruction and abundant code examples, you’ll explore the most challenging NLP issues and learn how to solve them with deep learning!

About the Technology

Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning. These breakthroughs, including recognizing patterns, inferring meaning from context, and determining emotional tone, are radically improving modern daily conveniences like web searches, social media feeds, and interactions with voice assistants. And they’re transforming the business world too!

A goldmine of unstructured textual data already exists, largely untapped simply because it doesn’t follow any predefined format. NLP is poised to conquer that data with its impressive abilities to scan for keywords and phrases and discern sentiment and preferences. And as the big data trend continues, opportunities to capitalize on the benefits of NLP abound as efforts are being made to ensure data is increasingly user-friendly. What’s more, this game-changing tech can dovetail with your business apps, offering potential for automated summaries, chatbots with near-human responses, and search that practically reads the user’s mind. All this is possible when deep learning meets natural language processing!

About the book

Deep Learning for Natural Language Processing teaches you to apply state-of-the-art deep learning approaches to natural language processing tasks. You’ll learn key NLP concepts like neural word embeddings, auto-encoders, part-of-speech tagging, parsing, and semantic inference. Then you’ll dive deeper into advanced topics including deep memory-based NLP, linguistic structure, and hyperparameters for deep NLP. Along the way, you’ll pick up emerging best practices and gain hands-on experience with a myriad of examples, all written in Python and the powerful Keras library. By the time you’re done reading this invaluable book, you’ll be solving a wide variety of NLP problems with cutting-edge deep learning techniques!
Table of Contents detailed table of contents

Part 1 Introduction

1 Deep learning for NLP

1.1 Overview of the book

1.2 A selection of machine learning methods for NLP

1.2.1 The perceptron

1.2.2 Support Vector Machines

1.2.3 Memory-based learning

1.3 Deep Learning

1.4 Vector representations of language

1.4.1 Representational vectors

1.4.2 Operational vectors

1.5 Vector sanitization

1.5.1 The Hashing trick

1.5.2 Vector normalization

1.6 Wrapping up

1.7 References

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

2.3 Wrapping up

Part 2 Deep NLP

3 Text embeddings

3.1 Embeddings

3.1.1 Embedding by hand: representational embeddings

3.1.2 Learning to embed: procedural embeddings

3.2 From words to vectors: word2vec

3.3 From documents to vectors: doc2vec

3.4 Wrapping up

3.5 External resources

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

4.5 Authorship attribution

4.5.1 Multilayer perceptrons

4.5.2 CNNs for text

4.6 Authorship verification

4.6.1 Siamese networks: network twins

4.7 Wrap up

5 Sequential NLP and memory

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

5.4 Conclusion

5.5 Further reading

5.6 Data and software resources

Part 3 Advanced topics

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

6.5 Semi-supervised memory networks: experiments and results

6.6 Summary

6.7 Code and data

6.8 Further reading

7 Attention

7.1 Neural attention

7.2 Data

7.3 Static attention: MLP

7.4 Temporal attention: LSTM

7.4.1 Experiments

7.5 Summary

7.6 Further reading

8 Multitask learning

8.1 Introduction

8.2 Data

8.3 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 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 Part-of-speech and named entity recognition data

8.5.1 Data handling

8.5.2 Hard parameter sharing

8.5.3 Soft parameter sharing

8.5.4 Mixed parameter sharing

8.6 Conclusions

8.7 Further reading

9 Transformers and Embeddings

10 Applications of Transformers


Appendix A: A random walk through NLP

A.1 Deep versus shallow linguistics

What's inside

  • An overview of NLP and deep learning
  • One-hot text representations
  • Word embeddings
  • Models for textual similarity
  • Sequential NLP
  • Semantic role labeling
  • Deep memory-based NLP
  • Linguistic structure
  • Hyperparameters for deep NLP

About the reader

For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required.

About the author

Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He’s the technical coordinator of two large European Union-funded research security-related projects. Professor dr. Raaijmakers holds an endowed chair for Communicative AI at Leiden University.

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