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.

What is NLP

Natural Language Processing (NLP) is both a modern computational technology and a method of investigating and evaluating claims about human language itself. Some prefer the term Computational Linguistics in order to capture this latter function, but NLP is a term that links back into the history of Artificial Intelligence (AI), the general study of cognitive function by computational processes, normally with an emphasis on the role of knowledge representations, that is to say the need for representations of our knowledge of the world in order to understand human language with computers. Natural Language Processing (NLP) is the use of computers to process written and spoken language for some practical, useful, purpose: to translate languages, to get information from the web on text data banks so as to answer questions, to carry on conversations with machines, so as to get advice about, say, pensions and so on. These are only examples of major types of NLP, and there is also a huge range of lesser but interesting applications, e.g. getting a computer to decide if one newspaper story has been rewritten from another or not. NLP is not simply applications but the core technical methods and theories that the major tasks above divide up into, such as Machine Learning techniques, which is automating the construction and adaptation of machine dictionaries, modeling human agents' beliefs and desires etc. This last is closer to Artificial Intelligence, and is an essential component of NLP if computers are to engage in realistic conversations: they must, like us, have an internal model of the humans they converse with