This book is about Natural Language Processing with python. A language is the native speech of a people such as English, Hindi, or Portuguese. In this book we are going to study how to make computers to understand human languages. You will learn how python libraries deals with unstructured data as we know that human communicate with computer in texts instead of tabular form. So this course is helping us to learn how we can install NTLK and how we use python libraries to make computers to understand unstructured data.
Natural language processing:
Natural language processing is a field that enable computers to understand natural human language usable by computer programs.
Natural Language Toolkit:
Natural Language Toolkit is a Python library that is used for NLP and Natural language toolkit supported by large network behind it. Natural language toolkit is also quite simple. Natural language toolkit is a very common natural language processing library that you can be use.
Why We Need Natural Language Toolkit:
Natural language toolkit is a great full Python package that offers a large number of natural languages algorithms. Natural language toolkit is inexpensive and quick method. It is open source and has large network behind it, and well described. Natural language toolkit make the computer to examine and understand the text. It enables computers to communicate with human in their language like it with Natural language toolkit computers can read and understand human text their speech and understand their demands.
Benefits of Natural language toolkit:
The best about Natural language toolkit is that it is quite simple to use. It takes long time to write algorithms but use of Natural language toolkit make it faster to write algorithms. The great thing about Natural language toolkit is that it has well-trained and excellent features which make computers to read and analyze data quickly. It is very helpful in education and research works.
- Firstly, we go through chapter one in which we learn Computing with Languages with Texts and Words then a closer Look at Python and back again and read about Computing with Language with Simple Statistics and at last we study about Automatic Natural Language Understanding
- The next thing we will discuss in this book is Accessing Text Corpora and Lexical Resources this is about the Conditional Frequency Distributions and some more Python and briefly discuss Reusing Code and WordNet
- In chapter three we will explain about Processing Raw Text and learn about Accessing Text from the Web and from Disk, how to Process text at the Lowest Level, how to Process text with Unicode and also go through Regular Expressions for Detecting and Word Patterns, we also read the Useful Applications of Regular Expressions and at the end of chapter we study about Regular Expressions for Tokenizing Text
- After that we will learn Writing Structured Programs and get Back to the Basics and a detailed look on Sequences, Questions of Style and also about Functions and how to Develop Program and Algorithm Design.
- Chapter five is about Categorizing and Tagging Words which includes Use of Tagger, Tagged Corpora and Mapping Words to Properties Using Python Dictionaries, also study How to Determine the Category of a Word
- Then we study about Learning to Classify Text it consists of Supervised Classification, Evaluation also Decision Trees and Patterns involves Modeling Linguistic
- Next we learn Extracting Information from Text in which we study about Table of Contents and Chunking also about Developing and Evaluating Chukers and go through Recursion in Linguistic Structure
- At the last of book, we look at Managing Linguistic Data this helps us to learn about Corpus Structure and their Life Cycle how to Acquire Data, how to Work with XML and Describe Language Resources Using OLAC Metadata.