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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.
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.
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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.
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
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
4.8.1 Semantic search
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 Retrieval (search)
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
12.8.3 When all else fails, search
12.8.4 Being popular
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.4.1 Vector space models and semantic search
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
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
