Mastering Natural Language Processing with Python

Maximize your NLP capabilities while creating amazing NLP projects in Python

About This Book

• Learn to implement various NLP tasks in Python
• Gain insights into the current and budding research topics of NLP
• This is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applications

Who This Book Is For

This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.

What You Will Learn

• Implement string matching algorithms and normalization techniques
• Implement statistical language modeling techniques
• Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
• Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
• Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
• Develop an NER-based system and understand and apply the concepts of sentiment analysis
• Understand and implement the concepts of Information Retrieval and text summarization
• Develop a Discourse Analysis System and Anaphora Resolution based system

In Detail

Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.

This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.

You will sequentially be guided through applying machine learning tools to develop various models. We’ll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.

Style and approach

This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.

Table of Contents

Chapter 1: Working with Strings
Chapter 2: Statistical Language Modeling
Chapter 3: Morphology – Getting Our Feet Wet
Chapter 4: Parts-of-Speech Tagging – Identifying words
Chapter 5: Parsing – Analyzing Training Data
Chapter 6: Semantic Analysis – Meaning Matters
Chapter 7: Sentiment Analysis – I Am Happy
Chapter 8: Information Retrieval – Accessing Information
Chapter 9: Discourse Analysis – Knowing Is Believing
Chapter 10: Evaluation of NLP Systems – Analyzing Performance