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 of features engineering

Here, we will discuss the challenges of features engineering for NLP applications. You must be thinking that we have a lot of options available in terms of tools and algorithms, so what is the most challenging part? Let's find out:

  • In the NLP domain, you can easily derive the features that are categorical features or basic NLP features. We have to convert these features into a numerical format. This is the most challenging part.
  • An effective way of converting text data into a numerical format is quite challenging. Here, the trial and error method may help you.
  • Although there are a couple of techniques that you can use, such as TF-IDF, one-hot encoding, ranking, co-occurrence matrix, word embedding, Word2Vec, and so on to convert your text data into a numerical format, there are not many ways, so people find this part challenging.
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