Book Image

Python Natural Language Processing

Book Image

Python Natural Language Processing

Overview of this book

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.
Table of Contents (13 chapters)

Challenges for the rule-based system

Let's look at some of the challenges in the RB approach:

  • It is not easy to mimic the behavior of a human.
  • Selecting or designing architecture is the critical part of the RB system.
  • In order to develop the RB system, you need to be an expert of the specific domain which generates rules for us. For NLP we need linguists who know how to analyze language.
  • Natural language is itself a challenging domain because it has so many exception cases and covering those exceptions using rules is also a challenging task, especially when you have a large amount of rules.
  • Arabic, Gujarati, Hindi, and Urdu are difficult to implement in the RB system because finding a domain expert for these languages is a difficult task. There are also less tools available for the described languages to implement the rules.
  • Time consumption of human effort is too high.
  • ...