Feature engineering is the most important part of developing NLP applications. Features are the input parameters for machine learning (ML) algorithms. These ML algorithms generate output based on the input features. Feature engineering is a kind of art and skill because it generates the best possible features, and choosing the best algorithm to develop NLP application requires a lot of effort and understanding about feature engineering as well as NLP and ML algorithms. In Chapter 2, Practical Understanding of Corpus and Dataset, we saw how data is gathered and what the different formats of data or corpus are. In Chapter 3, Understanding Structure of Sentences, we touched on some of the basic but important aspects of NLP and linguistics. We will use these concepts to derive features in this chapter. In Chapter 4, Preprocessing, we looked preprocessing...
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Python Natural Language Processing
By :
Python Natural Language Processing
By:
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)
Preface
Free Chapter
Introduction
Practical Understanding of a Corpus and Dataset
Understanding the Structure of a Sentences
Preprocessing
Feature Engineering and NLP Algorithms
Advanced Feature Engineering and NLP Algorithms
Rule-Based System for NLP
Machine Learning for NLP Problems
Deep Learning for NLU and NLG Problems
Advanced Tools
How to Improve Your NLP Skills
Customer Reviews
