Natural Language Processing in Action
Understanding, analyzing, and generating text with Python
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! You'll start with a mental model of how a computer learns to read and interpret language. Then, you'll discover how to train a Python-based NLP machine to recognize patterns and extract information from text. As you explore the carefully-chosen examples, you'll expand your machine's knowledge and apply it to a range of challenges, from building a search engine that can find documents based on their meaning rather than merely keywords, to training a chatbot that uses deep learning to answer questions and participate in a conversation.
Table of Contents takes you straight to the bookdetailed 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 (regular expressions)
1.4.2 A simple chatbot
1.4.3 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 A token improvement
2.2.2 Extending your vocabulary with n-grams
2.2.3 Normalizing your vocabulary
2.3 Sentiment
2.3.1 VADER — A rule-based sentiment analyzer
2.3.2 Naive Bayes — a machine learning sentiment analyzer
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
3.5 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 (LSA)
4.2.1 Our Thought Experiment Made Real
4.3 Singular Value Decomposition (SVD)
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 (PCA)
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 (LDA)
4.8 Topic Vector Power
4.8.1 Semantic Search
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 A Pythonic Neuron
5.1.5 Class is in Session
5.1.6 Logic is a Fun Thing to Learn
5.1.7 Next Step
5.1.8 Emergence from the From the First AI Winter
5.1.9 Backpropagation
5.1.10 Derivative All the Things
5.1.11 Let’s Go Skiing - The Error Surface
5.1.12 Off the Chair Lift, Onto the Slope
5.1.13 Let’s Shake Things Up a Bit
5.1.14 Keras: Neural Networks in Python
5.1.15 Onward and Deepward
5.1.16 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 the 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 Vector)
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
7.1.1 Word Order
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 We 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
8.3.1 Hyperparameters
8.4 Predicting
8.4.1 Statefulness
8.4.2 Two Way Street
8.4.3 What is this thing?
8.5 Summary
9 Improving Retention with Long Short-Term Memory Networks (LSTMs)
9.1 LSTM
9.1.1 Backpropagation Through Time
9.1.2 In Practice
9.1.3 Where does the rubber hit the road?
9.1.4 Dirty Data
9.1.5 Back to Our Dirty Data
9.1.6 Words are hard. Letters are easier.
9.1.7 My Turn to Talk
9.1.8 My Turn to Speak More Clearly
9.1.9 Learned How to Say, but not yet What.
9.1.10 Other Kinds of Memory
9.1.11 Going Deeper
9.2 Summary
10 Sequence to Sequence Models and Attention (Generative Models)
10.1 Sequence-to-Sequence Networks
10.2 How are seq2seq networks implemented?
10.2.1 Preparing your dataset for the sequence-to-sequence training
10.2.2 Sequence-to-Sequence in Keras
10.2.3 The Sequence-to-Sequence Encoder
10.2.4 The Sequence-to-Sequence 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 seq2seq networks
10.4.1 Preparing the corpus for our training
10.4.2 Building our 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 by using 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 Numbers
11.3.2 GPS Locations
11.3.3 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 split('.!?') Won’t 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 1. Pattern Matching
12.2.1 A Pattern-Matching Chatbot with Artificial Intelligence Markup Language (AIML)
12.3 2. Grounding
12.4 3. Retrieval (Search)
12.4.1 The Context Challenge
12.4.2 Example Retrieval-Based Chatbot
12.4.3 A Search-based Chatbot
12.5 4. Generative Models
12.5.1 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/Likable
12.8.3 When All Else Fails, Search
12.8.4 Being Popular
12.8.5 Be a Connector/Networker
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 Rent 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: Acquiring Words
Appendix B: You Slice, I Choose
Appendix C: Vectors and Matrices (Linear Algebra)
Appendix D: Machine Learning
About the Technology
Most humans are pretty good at reading and interpreting text; computers...not so much. Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and you see examples of it in everything from chatbots to the speech-recognition software on your phone. Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. NLP promises to help you improve customer interactions, save cost, and reinvent text-intensive applications like search or product support.
What's inside
- Working with Keras, TensorFlow, Gensim, scikit-learn, and more
- Parsing and normalizing text
- Rule-based (Grammar) NLP
- Data-based (Machine Learning) NLP
- Deep Learning NLP
- End-to-end chatbot pipeline with training data
- Scalable NLP pipelines
- Hyperparameter optimization algorithms
About the reader
While all examples are written in Python, experience with any modern programming language will allow readers to get the most from this book. A basic understanding of machine learning will also be helpful.
About the authors
Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.
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