Getting started with natural language processing: a friendly introduction using python

Hit the ground running with this in-depth introduction to the NLP skills and techniques that allow your computers to speak human.InGetting Started with Natural Language Processingyou’ll learn about: Fundamental concepts and algorithms of NLP Useful Python libraries for NLP Building a search algorithm Extracting information from raw text Predicting sentiment of an input text Author profiling Topic labeling Named entity recognitionGetting Started with Natural Language Processingis an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. If you’re a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technology From smart speakers to customer service chatbots, apps that understand text and speech are everywhere. Natural language processing, or NLP, is the key to this powerful form of human/computer interaction. And a new generation of tools and techniques make it easier than ever to get started with NLP!About the bookGetting Started with Natural Language Processingteaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you’ll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you’ve previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get started—even if you have no background in machine learning!What's inside Fundamental concepts and algorithms of NLP Extracting information from raw text Useful Python libraries Topic labeling Building a search algorithmAbout the reader You’ll need basic Python skills. No experience with NLP required.About the authorEkaterina Kochmaris a lecturer at the Department of Computer Science of the University of Bath, where she is part of the AI research group.Table of Contents 1 Introduction 2 Your first NLP example 3 Introduction to information search 4 Information extraction 5 Author profiling as a machine-learning task 6 Linguistic feature engineering for author profiling 7 Your first sentiment analyzer using sentiment lexicons 8 Sentiment analysis with a data-driven approach 9 Topic analysis 10 Topic modeling 11 Named-entity recognition Getting Started with Natural Language Processing brief contents contents preface acknowledgments about this book Who should read this book How this book is organized: A road map About the code liveBook discussion forum Other online resources about the author about the cover illustration 1 Introduction 1.1 A brief history of NLP 1.2 Typical tasks 1.2.1 Information search 1.2.2 Advanced information search: Asking the machine precise questions 1.2.3 Conversational agents and intelligent virtual assistants 1.2.4 Text prediction and language generation 1.2.5 Spam filtering 1.2.6 Machine translation 1.2.7 Spell- and grammar checking Summary Solution to exercise 1.1 2 Your first NLP example 2.1 Introducing NLP in practice: Spam filtering 2.2 Understanding the task 2.2.1 Step 1: Define the data and classes 2.2.2 Step 2: Split the text into words 2.2.3 Step 3: Extract and normalize the features 2.2.4 Step 4: Train a classifier 2.2.5 Step 5: Evaluate the classifier 2.3 Implementing your own spam filter 2.3.1 Step 1: Define the data and classes 2.3.2 Step 2: Split the text into words 2.3.3 Step 3: Extract and normalize the features 2.3.4 Step 4: Train the classifier 2.3.5 Step 5: Evaluate your classifier 2.4 Deploying your spam filter in practice Summary Solutions to miscellaneous exercises 3 Introduction to information search 3.1 Understanding the task 3.1.1 Data and data structures 3.1.2 Boolean search algorithm 3.2 Processing the data further 3.2.1 Preselecting the words that matter: Stopwords removal 3.2.2 Matching forms of the same word: Morphological processing 3.3 Information weighing 3.3.1 Weighing words with term frequency 3.3.2 Weighing words with inverse document frequency 3.4 Practical use of the search algorithm 3.4.1 Retrieval of the most similar documents 3.4.2 Evaluation of the results 3.4.3 Deploying search algorithm in practice Summary Solutions to miscellaneous exercises 4 Information extraction 4.1 Use cases 4.1.1 Case 1 4.1.2 Case 2 4.1.3 Case 3 4.2 Understanding the task 4.3 Detecting word types with part-of-speech tagging 4.3.1 Understanding word types 4.3.2 Part-of-speech tagging with spaCy 4.4 Understanding sentence structure with syntactic parsing 4.4.1 Why sentence structure is important 4.4.2 Dependency parsing with spaCy 4.5 Building your own information extraction algorithm Summary Solutions to miscellaneous exercises 5 Author profiling as a machine-learning task 5.1 Understanding the task 5.1.1 Case 1: Authorship attribution 5.1.2 Case 2: User profiling 5.2 Machine-learning pipeline at first glance 5.2.1 Original data 5.2.2 Testing generalization behavior 5.2.3 Setting up the benchmark 5.3 A closer look at the machine-learning pipeline 5.3.1 Decision Trees classifier basics 5.3.2 Evaluating which tree is better using node impurity 5.3.3 Selection of the best split in Decision Trees 5.3.4 Decision Trees on language data Summary Solutions to miscellaneous exercises 6 Linguistic feature engineering for author profiling 6.1 Another close look at the machine-learning pipeline 6.1.1 Evaluating the performance of your classifier 6.1.2 Further evaluation measures 6.2 Feature engineering for authorship attribution 6.2.1 Word and sentence length statistics as features 6.2.2 Counts of stopwords and proportion of stopwords as features 6.2.3 Distributions of parts of speech as features 6.2.4 Distribution of word suffixes as features 6.2.5 Unique words as features 6.3 Practical use of authorship attribution and user profiling Summary 7 Your first sentiment analyzer using sentiment lexicons 7.1 Use cases 7.2 Understanding your task 7.2.1 Aggregating sentiment score with the help of a lexicon 7.2.2 Learning to detect sentiment in a data-driven way 7.3 Setting up the pipeline: Data loading and analysis 7.3.1 Data loading and preprocessing 7.3.2 A closer look into the data 7.4 Aggregating sentiment scores with a sentiment lexicon 7.4.1 Collecting sentiment scores from a lexicon 7.4.2 Applying sentiment scores to detect review polarity Summary Solutions to exercises 8 Sentiment analysis with a data-driven approach 8.1 Addressing multiple senses of a word with SentiWordNet 8.2 Addressing dependence on context with machine learning 8.2.1 Data preparation 8.2.2 Extracting features from text 8.2.3 Scikit-learn’s machine-learning pipeline 8.2.4 Full-scale evaluation with cross-validation 8.3 Varying the length of the sentiment-bearing features 8.4 Negation handling for sentiment analysis 8.5 Further practice Summary 9 Topic analysis 9.1 Topic classification as a supervised machine-learning task 9.1.1 Data 9.1.2 Topic classification with Naïve Bayes 9.1.3 Evaluation of the results 9.2 Topic discovery as an unsupervised machine-learning task 9.2.1 Unsupervised ML approaches 9.2.2 Clustering for topic discovery 9.2.3 Evaluation of the topic clustering algorithm Summary Solutions to miscellaneous exercises 10 Topic modeling 10.1 Topic modeling with latent Dirichlet allocation 10.1.1 Exercise 10.1: Question 1 solution 10.1.2 Exercise 10.1: Question 2 solution 10.1.3 Estimating parameters for the LDA 10.1.4 LDA as a generative model 10.2 Implementation of the topic modeling algorithm 10.2.1 Loading the data 10.2.2 Preprocessing the data 10.2.3 Applying the LDA model 10.2.4 Exploring the results Summary Solutions to miscellaneous exercises 11 Named-entity recognition 11.1 Named entity recognition: Definitions and challenges 11.1.1 Named entity types 11.1.2 Challenges in named entity recognition 11.2 Named-entity recognition as a sequence labeling task 11.2.1 The basics: BIO scheme 11.2.2 What does it mean for a task to be sequential? 11.2.3 Sequential solution for NER 11.3 Practical applications of NER 11.3.1 Data loading and exploration 11.3.2 Named entity types exploration with spaCy 11.3.3 Information extraction revisited 11.3.4 Named entities visualization Summary Conclusion Solutions to miscellaneous exercises Appendix—Installation instructions index Symbols A B C D E F G H I J K L M N O P Q R S T U V W Z Hit the ground running with this in-depth introduction to the NLP skills and techniques that allow your computers to speak human. In Getting Started with Natural Language Processing you’ll learn about: Fundamental concepts and algorithms of NLP Useful Python libraries for NLP Building a search algorithm Extracting information from raw text Predicting sentiment of an input text Author profiling Topic labeling Named entity recognition Getting Started with Natural Language Processing is an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. If you’re a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology From smart speakers to customer service chatbots, apps that understand text and speech are everywhere. Natural language processing, or NLP, is the key to this powerful form of human/computer interaction. And a new generation of tools and techniques make it easier than ever to get started with NLP! About the book Getting Started with Natural Language Processing teaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you’ll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you’ve previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get started—even if you have no background in machine learning! What's inside Fundamental concepts and algorithms of NLP Extracting information from raw text Useful Python libraries Topic labeling Building a search algorithm About the reader You’ll need basic Python skills. No experience with NLP required. About the author Ekaterina Kochmar is a lecturer at the Department of Computer Science of the University of Bath, where she is part of the AI research group. Table of Contents 1 Introduction 2 Your first NLP example 3 Introduction to information search 4 Information extraction 5 Author profiling as a machine-learning task 6 Linguistic feature engineering for author profiling 7 Your first sentiment analyzer using sentiment lexicons 8 Sentiment analysis with a data-driven approach 9 Topic analysis 10 Topic modeling 11 Named-entity recognition Hit the ground running with this in-depth introduction to the NLP skills and techniques that allow your computers to speak human. In Getting Started with Natural Language Processing you'll learn Fundamental concepts and algorithms of NLP Useful Python libraries for NLP Building a search algorithm Extracting information from raw text Predicting sentiment of an input text Author profiling Topic labeling Named entity recognition Getting Started with Natural Language Processing is an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. If you're a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology From smart speakers to customer service chatbots, apps that understand text and speech are everywhere. Natural language processing, or NLP, is the key to this powerful form of human/computer interaction. And a new generation of tools and techniques make it easier than ever to get started with NLP! About the book Getting Started with Natural Language Processing teaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you'll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you've previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get startedeven if you have no background in machine learning! What's inside Fundamental concepts and algorithms of NLP Extracting information from raw text Useful Python libraries Topic labeling Building a search algorithm About the reader You'll need basic Python skills. No experience with NLP required. About the author Ekaterina Kochmar is a lecturer at the Department of Computer Science of the University of Bath and a cofounder and the chief scientific officer of Korbit AI. Table of Contents 1 Introduction 2 Your first NLP example 3 Introduction to information search 4 Information extraction 5 Author profiling as a machine-learning task 6 Linguistic feature engineering for author profiling 7 Your first sentiment analyzer using sentiment lexicons 8 Sentiment analysis with a data-driven approach 9 Topic analysis 10 Topic modeling 11 Named-entity recognition