Thursday, September 1, 2016

Mastering Natural Language Processing with Python, Packt Publishing

In this book, we will learn how to implement various tasks of NLP in Python and
gain insight to the current and budding research topics of NLP. This book is a
comprehensive step-by-step guide to help students and researchers to create their own projects based on real-life applications.

What this book covers
Chapter 1, Working with Strings, explains how to perform preprocessing tasks on text, such as tokenization and normalization, and also explains various string
matching measures.
Chapter 2, Statistical Language Modeling, covers how to calculate word frequencies and perform various language modeling techniques.
Chapter 3, Morphology – Getting Our Feet Wet, talks about how to develop a stemmer, morphological analyzer, and morphological generator.
Chapter 4, Parts-of-Speech Tagging – Identifying Words, explains Parts-of-Speech tagging and statistical modeling involving the n-gram approach.
Chapter 5
, Parsing – Analyzing Training Data, provides information on the concepts of Tree bank construction, CFG construction, the CYK algorithm, the Chart Parsing algorithm, and transliteration.
Chapter 6, Semantic Analysis – Meaning Matters, talks about the concept and application of Shallow Semantic Analysis (that is, NER) and WSD using Wordnet.
Chapter 7, Sentiment Analysis – I Am Happy, provides information to help you
understand and apply the concepts of sentiment analysis.
Chapter 8, Information Retrieval – Accessing Information, will help you understand and apply the concepts of information retrieval and text summarization.
Chapter 9, Discourse Analysis – Knowing Is Believing, develops a discourse analysis system and anaphora resolution-based system.
Chapter 10, Evaluation of NLP Systems – Analyzing Performance, talks about
understanding and applying the concepts of evaluating NLP systems.