Natural Language Processing in Action is your guide to creating machines that understand human language. 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 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.
Acknowledgments
Part 1: Finding Meaning in Natural Language
1 The Language of Thought
1.1 The Magic
1.2 Practical Applications
1.3 What is Driving NLP Advances?
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
2.1 Building your vocabulary through tokenization
2.1 A Token Improvement
2.2.1 Contractions
2.3 Extending your vocabulary with N-grams
2.3.1 What are N-grams?
2.3.2 Stopwords
2.4 Normalizing your vocabulary
2.4.1 Case normalization
2.4.2 Stemming
2.4.3 Lemmatization
2.4.4 Use Cases
2.5 Summary
3 Math with Words
3.1 Bag of Words
3.2 Vectorizing
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
3.5 Summary
4 Finding Meaning in Word Counts
4.1 From Word Counts to Topics
4.2 Latent Semantic Analysis (LSA)
4.3 Singular Value Decomposition (SVD)
4.4 Truncated SVD
4.5 SciKit-Learn vs gensim
4.5.1 PCA on a Point Cloud
4.5.2 Let’s Stop Horsing Around and Get Back to NLP
4.5.3 PCA on SMS Messages
4.5.4 Truncated SVD
4.5.5 Which one is Better for Spam Classification?
4.5.6 A Simple Classifier
4.6 Latent Dirichlet Allocation (LDiA)
4.6.1 The LDiA Idea
4.6.2 LDiA Topic Model for SMS Messages
4.6.3 LDiA + LDA = Spam Classifier
4.6.4 A Fairer Comparison: 32 LDiA Topics
4.7 Distance and Similarity
4.8 Steering
4.8.1 Linear Discriminant Analysis (LDA)
4.9 Topic Vector Power
4.9.1 Semantic Search
4.10 "Like" Prediction
4.11 Summary
Part 2: Deeper Learning
5 Baby Steps with Neural Networks
6 Reasoning with Word Vectors (Word2vec)
6.1 Vector-Oriented Reasoning
6.2 Applications for Word Vectors
6.3 How to compute the Word Vector Representations?
6.3.1 Skip-gram Approach
6.3.2 Continous Bag of Words Approach
6.3.3 Skip-gram vs. Continous Bag of Words: When to use which approach
6.3.4 Computational Tricks of Word2vec
6.4 How to use Gensim’s Word2vec module?
6.5 How to generate your own Word vector representations?
6.5.1 Preprocess Steps
6.5.2 Train your domain-specific Word2vec model
6.6 Word2vec vs GloVe (Global Vector)
6.7 Word2vec vs LSA
6.8 Doc2Vec
6.9 Summary
7 Getting Words in Order with Convolutional Neural Networks (CNNs)
8 Loopy (Recurrent) Neural Networks (RNNs)
9 Improving Retention with Long Short-Term Memory Networks (LSTMs)
10 Attention Networks, Translation, and Sequence to Sequence Models
Part 3 Getting Real
11 Information Extraction
12 Getting Chatty
13 Generative Models
14 Scaling Up
Appendixes
Appendix A: A Your NLP Tools
Appendix B: B Playful Python
Appendix C: C Vectors and Matrices (Linear Algebra)
Appendix D: 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
- 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
A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will 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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