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.
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.
For all the chapters, Python 2.7 or 3.2+ is used. NLTK 3.0 must be installed either on a 32-bit machine or 64-bit machine. The operating system that is required is Windows/Mac/Unix.
This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "For tokenization of French text, we will use the french.pickle file."
A block of code is set as follows:
>>> import nltk >>> text=" Welcome readers. I hope you find it interesting. Please do reply." >>> from nltk.tokenize import sent_tokenize
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