In this chapter, we have seen many concepts and tools that are widely used in the NLP domain. All of these concepts are the basic building blocks of features engineering. You can use any of these techniques when you want to generate features in order to generate NLP applications. We have looked at how parse, POS taggers, NER, n-grams, and bag-of-words generate Natural Language-related features. We have also explored the how they are built and what the different ways to tweak some of the existing tools are in case you need custom features to develop NLP applications. Further, we have seen basic concepts of linear algebra, statistics, and probability. We have also seen the basic concepts of probability that will be used in ML algorithms in the future. We have looked at some cool concepts such as TF-IDF, indexing, ranking, and so on, as well as the language model as part...
Python Natural Language Processing
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.
Related Content you might be interested in
Current Title:
Python Natural Language Processing
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