The Natural Language Processing Workshop

封面

版权信息

Preface

1. Introduction to Natural Language Processing

Introduction

History of NLP

Text Analytics and NLP

Various Steps in NLP

Word Sense Disambiguation

Sentence Boundary Detection

Kick Starting an NLP Project

Summary

2. Feature Extraction Methods

Introduction

Types of Data

Cleaning Text Data

Feature Extraction from Texts

Finding Text Similarity – Application of Feature Extraction

Summary

3. Developing a Text Classifier

Introduction

Machine Learning

Supervised Learning

Developing a Text Classifier

Building Pipelines for NLP Projects

Saving and Loading Models

Summary

4. Collecting Text Data with Web Scraping and APIs

Introduction

Collecting Data by Scraping Web Pages

Dealing with Semi-Structured Data

Summary

5. Topic Modeling

Introduction

Topic Discovery

Topic-Modeling Algorithms

Key Input Parameters for LSA Topic Modeling

Hierarchical Dirichlet Process (HDP)

Summary

6. Vector Representation

Introduction

What Is a Vector?

Summary

7. Text Generation and Summarization

Introduction

Generating Text with Markov Chains

Text Summarization

Key Input Parameters for TextRank

Recent Developments in Text Generation and Summarization

Practical Challenges in Extractive Summarization

Summary

8. Sentiment Analysis

Introduction

Tools Used for Sentiment Analysis

The textblob library

Understanding Data for Sentiment Analysis

Training Sentiment Models

Summary

Appendix

1. Introduction to Natural Language Processing

2. Feature Extraction Methods

3. Developing a Text Classifier

4. Collecting Text Data with Web Scraping and APIs

5. Topic Modeling

6. Vector Representation

7. Text Generation and Summarization