Natural Language Processing in Action
Understanding, analyzing, and generating text with Python
Hobson Lane, Cole Howard, Hannes Hapke
Foreword by Dr. Arwen Griffioen
  • March 2019
  • ISBN 9781617294631
  • 544 pages
  • printed in black & white

Learn both the theory and practical skills needed to go beyond merely understanding the inner workings of NLP, and start creating your own algorithms or models.

From the Foreword by Dr. Arwen Griffioen, Zendesk

Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI.

Table of Contents detailed table of contents

Acknowledgments

Part 1: Wordy machines

1 Packets of thought (NLP overview)

1.1 Natural language vs. programming language

1.2 The magic

1.2.1 Machines that converse

1.2.2 The math

1.3 Practical applications

1.4 Language through a computer’s "eyes"

1.4.1 The language of locks

1.4.2 Regular expressions

1.4.3 A simple chatbot

1.4.4 Another way

1.5 A brief overflight of hyperspace

1.6 Word order and grammar

1.7 A chatbot natural language pipeline

1.8 Processing in depth

1.9 Natural language IQ

1.10 Summary

2 Build your vocabulary (word tokenization)

2.1 Challenges (a preview of stemming)

2.2 Building your vocabulary with a tokenizer

2.2.1 Dot product

2.2.2 Measuring bag-of-words overlap

2.2.3 A token improvement

2.2.4 Extending your vocabulary with n-grams

2.2.5 Normalizing your vocabulary

2.3 Sentiment

2.3.1 VADER — A rule-based sentiment analyzer

2.3.2 Naive Bayes

2.4 Summary

3 Math with words (TF-IDF vectors)

3.1 Bag of words

3.2 Vectorizing

3.2.1 Vector spaces

3.3 Zipf’s Law

3.4 Topic modeling

3.4.1 Return of Zipf

3.4.2 Relevance ranking

3.4.3 Tools

3.4.4 Alternatives

3.4.5 Okapi BM25

3.4.6 What’s next

3.5 Summary

4 Finding meaning in word counts (semantic analysis)

4.1 From word counts to topic scores

4.1.1 TF-IDF vectors and lemmatization

4.1.2 Topic vectors

4.1.3 Thought experiment

4.1.4 An algorithm for scoring topics

4.1.5 An LDA classifier

4.2 Latent semantic analysis

4.2.1 Your thought experiment made real

4.3 Singular value decomposition

4.3.1 U — left singular vectors

4.3.2 S — singular values

4.3.3 VT — right singular vectors

4.3.4 SVD matrix orientation

4.3.5 Truncating the topics

4.4 Principal component analysis

4.4.1 PCA on 3D vectors

4.4.2 Stop horsing around and get back to NLP

4.4.3 Using PCA for SMS message semantic analysis

4.4.4 Using truncated SVD for SMS message semantic analysis

4.4.5 How well does LSA work for spam classification?

4.5 Latent Dirichlet allocation (LDiA)

4.5.1 The LDiA idea

4.5.2 LDiA topic model for SMS messages

4.5.3 LDiA + LDA = spam classifier

4.5.4 A fairer comparison: 32 LDiA topics

4.6 Distance and similarity

4.7 Steering with feedback

4.7.1 Linear discriminant analysis

4.8 Topic vector power

4.8.2 Improvements

4.9 Summary

Part 2: Deeper learning (neural networks)

5 Baby steps with neural networks (perceptrons and backpropagation)

5.1 Neural networks, the ingredient list

5.1.1 Perceptron

5.1.2 A numerical perceptron

5.1.3 Detour through bias

5.1.4 Let’s go skiing — the error surface

5.1.5 Off the chair lift, onto the slope

5.1.6 Let’s shake things up a bit

5.1.7 Keras: Neural networks in Python

5.1.8 Onward and deepward

5.1.9 Normalization: Input with style

5.2 Summary

6 Reasoning with word vectors (Word2vec)

6.1 Semantic queries and analogies

6.1.1 Analogy questions

6.2 Word vectors

6.2.1 Vector-oriented reasoning

6.2.2 How to compute Word2Vec representations

6.2.3 How to use the gensim.word2vec module

6.2.4 How to generate your own Word vector representations

6.2.5 Word2vec vs GloVe (Global Vectors)

6.2.6 fastText

6.2.7 Word2vec vs LSA

6.2.8 Visualizing word relationships

6.2.9 Unnatural words

6.2.10 Document similarity with Doc2vec

6.3 Summary

7 Getting words in order with convolutional neural networks (CNNs)

7.1 Learning meaning

7.2 Toolkit

7.3 Convolutional neural nets

7.3.1 Building blocks

7.3.2 Step size

7.3.3 Filter composition

7.3.4 Padding

7.3.5 Learning

7.4 Narrow windows indeed

7.4.1 Implementation in Keras: Prepping the data

7.4.2 Convolutional neural network architecture

7.4.3 Pooling

7.4.4 Dropout

7.4.5 The cherry on the sundae

7.4.6 Let’s get to learning (training)

7.4.7 Using the model in a pipeline

7.4.8 Where do you go from here?

7.5 Summary

8 Loopy (recurrent) neural networks (RNNs)

8.1 Remembering with recurrent networks

8.1.1 Backpropagation through time

8.1.2 When do we update what?

8.1.3 Recap

8.1.4 There’s always a catch

8.1.5 Recurrent neural net with Keras

8.2 Putting things together

8.3 Let’s get to learning our past selves

8.4 Hyperparameters

8.5 Predicting

8.5.1 Statefulness

8.5.2 Two-way street

8.5.3 What is this thing?

8.6 Summary

9 Improving retention with long short-term memory networks

9.1 LSTM

9.1.1 Backpropagation through time

9.1.2 Where does the rubber hit the road?

9.1.3 Dirty data

9.1.4 Back to the dirty data

9.1.5 Words are hard. Letters are easier.

9.1.6 My turn to chat

9.1.7 My turn to speak more clearly

9.1.8 Learned how to say, but not yet what

9.1.9 Other kinds of memory

9.1.10 Going deeper

9.2 Summary

10 Sequence-to-sequence models and attention

10.1 Encoder-decoder architecture

10.1.1 Decoding thought

10.1.2 Look familiar?

10.1.3 Sequence-to-sequence conversation

10.1.4 LSTM review

10.2 Assembling a sequence-to-sequence pipeline

10.2.1 Preparing your dataset for the sequence-to-sequence training

10.2.2 Sequence-to-sequence model in Keras

10.2.3 Sequence encoder

10.2.4 Thought decoder

10.2.5 Assembling the sequence-to-sequence network

10.3 Training the sequence-to-sequence network

10.3.1 Generate output sequences

10.4 Building a chatbot using sequence-to-sequence networks

10.4.1 Preparing the corpus for your training

10.4.2 Building your character dictionary

10.4.3 Generate one-hot encoded training sets

10.4.4 Train your sequence-to-sequence chatbot

10.4.5 Assemble the model for sequence generation

10.4.6 Predicting a sequence

10.4.7 Generating a response

10.4.8 Converse with your chatbot

10.5 Enhancements

10.5.1 Reduce training complexity with bucketing

10.5.2 Paying attention

10.6 In the real world

10.7 Summary

Part 3: Getting real (real-world NLP challenges)

11 Information extraction (named entity extraction and question answering)

11.1 Named entities and relations

11.1.1 A knowledge base

11.1.2 Information extraction

11.2 Regular patterns

11.2.1 Regular expressions

11.2.2 Information extraction as ML feature extraction

11.3 Information worth extracting

11.3.1 Extracting GPS locations

11.3.2 Extracting dates

11.4 Extracting relationships (relations)

11.4.1 POS tagging

11.4.2 Entity name normalization

11.4.3 Relation normalization and extraction

11.4.4 Word patterns

11.4.5 Segmentation

11.4.6 Why won’t split('.!?') work?

11.4.7 Sentence segmentation with regular expressions

11.5 In the real world

11.6 Summary

12 Getting chatty (dialog engines)

12.1 Language skill

12.1.1 Modern approaches

12.1.2 A hybrid approach

12.2 Pattern-matching approach

12.2.1 A pattern-matching chatbot with AIML

12.2.2 A network view of pattern matching

12.3 Grounding

12.4.1 The context challenge

12.4.2 Example retrieval-based chatbot

12.4.3 A search-based chatbot

12.5 Generative models

12.5.1 Chat about NLPIA

12.5.2 Pros and cons of each approach

12.6 Four-wheel drive

12.6.1 The Will to succeed

12.7 Design process

12.8 Trickery

12.8.1 Ask questions with predictable answers

12.8.2 Be entertaining

12.8.5 Be a connector

12.8.6 Getting emotional

12.9 In the real world

12.10 Summary

13 Scaling up (optimization, parallelization, and batch processing)

13.1 Too much of a good thing (data)

13.2 Optimizing NLP algorithms

13.2.1 Indexing

13.2.2 Advanced indexing

13.2.3 Advanced indexing with Annoy

13.2.4 Why use approximate indexes at all?

13.2.5 An indexing workaround: Discretizing

13.3 Constant RAM algorithms

13.3.1 Gensim

13.3.2 Graph computing

13.4 Parallelizing your NLP computations

13.4.1 Training NLP models on GPUs

13.4.2 Renting vs. buying

13.4.3 GPU rental options

13.4.4 Tensor processing units

13.5 Reducing the memory footprint during model training

13.6 Gaining model insights with TensorBoard

13.6.1 How to visualize word embeddings

13.7 Summary

Appendixes

Appendix A: Your NLP tools

A.1 Anaconda3

A.2 Install NLPIA

A.3 IDE

A.4 Ubuntu package manager

A.5 Mac

A.5.1 A Mac package manager

A.5.2 Some packages

A.5.3 Tuneups

A.6 Windows

A.6.1 Get Virtual

A.7 NLPIA automagic

Appendix B: Playful Python and regular expressions

B.1 Working with strings

B.1.1 String types (str and bytes)

B.1.2 Templates in Python (.format())

B.2 Mapping in Python (dict and OrderedDict)

B.3 Regular expressions

B.3.1 | — "OR"

B.3.2 () — Groups

B.3.3 [] — Character classes

B.4 Style

B.5 Mastery

Appendix C: Vectors and matrices (linear algebra fundamentals)

C.1 Vectors

C.1.1 Distances

Appendix D: Machine learning tools and techniques

D.1 Data selection and avoiding bias

D.2 How fit is fit?

D.3 Knowing is half the battle

D.4 Cross-fit training

D.5 Holding your model back

D.5.1 Regularization

D.5.2 Dropout

D.5.3 Batch normalization

D.6 Imbalanced training sets

D.6.1 Oversampling

D.6.2 Undersampling

D.6.3 Augmenting your data

D.7 Performance metrics

D.7.1 Measuring classifier performance

D.7.2 Measuring regressor performance

D.8 Pro tips

Appendix E: Resources

E.1 Applications and project ideas

E.2 Courses and tutorials

E.3 Tools and Packages

E.4 Research papers and talks

E.4.2 Finance

E.4.3 Question answering systems

E.4.4 Deep learning

E.4.5 LSTMs and RNNs

E.5 Competitions and awards

E.6 Datasets

E.7 Search engines

E.7.1 Search algorithms

E.7.2 Open source search engines

E.7.3 Open source full-text indexers

E.7.4 Manipulative search engines

E.7.5 Less manipulative search engines

E.7.6 Distributed search engines

Appendix F: Glossary

Acronyms

Terms

Appendix G: Setting up your AWS GPU

G.1 Steps to create your AWS GPU instance

G.1.1 Cost control

Appendix H: Locality sensitive hashing

H.1 High-dimensional vectors are different

H.1.1 Vector space indexes and hashes

H.1.2 High-dimensional thinking

H.2 High-dimensional indexing

H.2.1 Locality sensitive hashing

H.2.2 Approximate nearest neighbors

H.3 "Like" prediction

About the Technology

Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.

About the book

Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.

What's inside

  • Some sentences in this book were written by NLP! Can you guess which ones?
  • Working with Keras, TensorFlow, gensim, and scikit-learn
  • Rule-based and data-based NLP
  • Scalable pipelines

About the reader

This book requires a basic understanding of deep learning and intermediate Python skills.

About the authors

Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production.


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