Natural Language Processing For Hackers
Natural Language Processing For Hackers
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Natural Language Processing For Hackers

Last updated on 2018-07-09

About the Book

This is not your typical research oriented book that exposes the theoretical approach and uses clean datasets that you can only find in introductory courses and never in the real world. This is a hands-on, practical course on getting started with Natural Language Processing and learning key concepts while coding. No guesswork required.

Throughout the book you'll get to touch some of the most important and practical areas of Natural Language Processing. Everything you do will have a working result.

Here are some things you will get to tackle

  • Building your own Text Analysis engine
  • Understanding how data-gathering works in the real world
  • Building a Twitter listener that performs Sentiment Analysis on a certain subject
  • Understanding how the classic NLP tools are actually built, enabling you to build your own: Part Of Speech Tagger, Shallow Parser, Named Entity Extractor and Dependency Parser
  • Cleaning and standardising messy datasets
  • Understanding how to fine tune Natural Language models
  • Learn how chatbots work

The book contains complete code snippets and step-by-step examples. No need to fill in the blanks or wonder what the author meant. Everything is written in concise, easy-to-read Python 3 code.

Table of Contents

  •  
    •  
      • Preface
    • Introduction
      • What is Natural Language Processing?
      • Challenges in Natural Language Processing
      • What makes this book different?
  • Part 1: Introduction to NLTK
    • NLTK Fundamentals
      • Installing NLTK
      • Splitting Text
      • Building a vocabulary
      • Fun with Bigrams and Trigrams
      • Part Of Speech Tagging
      • Named Entity Recognition
    • Getting started with Wordnet
      • Wordnet Structure
      • Lemma Operations
    • Lemmatizing and Stemming
      • How stemmers work
      • How lemmatizers work
  • Part 2: Create a Text Analysis service
    • Introduction to Machine Learning
      • A Practical Machine Learning Example
    • Getting Started with Scikit-Learn
      • Installing Scikit-Learn and building a dataset
      • Training a Scikit-Learn Model
      • Making Predictions
    • Finding the data
      • Existing corpora
      • Ideas for Gathering Data
      • Getting the Data
    • Learning to Classify Text
      • Text Feature Extractor
      • Scikit-Learn Feature Extraction
      • Text Classification with Naive Bayes
    • Persisting models
    • Building the API
      • Building a Flask API
      • Deploy to Heroku
  • Part 3: Create a Social Media Monitoring Service
    • Basics of Sentiment Analysis
      • Be Aware of Negations
      • Machine Learning doesn’t get Humour
      • Multiple and Mixed Sentiments
      • Non-Verbal Communication
    • Twitter Sentiment Data
      • Twitter Corpora
      • Other Sentiment Analysis Corpora
      • Building a Tweets Dataset
      • Sentiment Analysis - A First Attempt
      • Better Tokenization
    • Fine Tuning
      • Try a different classifier
      • Use Ngrams Instead of Words
      • Using a Pipeline
      • Cross Validation
      • Grid Search
      • Picking the Best Tokenizer
    • Building the Twitter Listener
    • Classification Metrics
      • Binary Classification
    • Multi-Class Metrics
      • The Confusion Matrix
  • Part 4: Build Your Own NLP Toolkit
    • Build Your Own Part-Of-Speech Tagger
      • Part-Of-Speech Corpora
      • Building Toy Models
      • About Feature Extraction
      • Using the NLTK Base Classes
      • Writing the Feature Extractor
      • Training the Tagger
      • Out-Of-Core Learning
    • Build a Chunker
      • IOB Tagging
      • Implementing the Chunk Parser
      • Chunker Feature Detection
    • Build a Named Entity Extractor
      • NER Corpora
      • The Groningen Meaning Bank Corpus
      • Feature Detection
      • NER Training
    • Build a Dependency Parser
      • Understanding the Problem
      • Greedy Transition-Based Parsing
      • Dependency Dataset
      • Writing the Dependency Parser Class
    • Adding Labels to the Parser
      • Learning to Label Dependencies
      • Training our Labelled Dependency Parser
  • Part 5: Build Your Own Chatbot Engine
    • General Architecture
      • Train the Platform via Examples
      • Action Handlers
    • Building the Core
      • Chatbot Base Class and Training Set
      • Training the Chatbot
      • Everything together
    • MovieBot
      • The Movie DB API
      • Small-Talk Handlers
      • Simple Handlers
      • Execution Handlers
    • MovieBot on Facebook
      • Installing ngrok
      • Setting up Facebook
      • Trying it Out
      • What Next?

About the Author

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