Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
Book Image

Hands-On Python Natural Language Processing

By: Aman Kedia, Mayank Rasu

Overview of this book

Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
Table of Contents (16 chapters)
1
Section 1: Introduction
4
Section 2: Natural Language Representation and Mathematics
9
Section 3: NLP and Learning
Word Embeddings and Distance Measurements for Text

In Chapter 4, Transforming Text into Data Structures, we discussed the bag-of-words and term-frequency and inverse document frequency-based methods to represent text in the form of numbers. These methods mostly rely on the syntactical aspects of a word in terms of its presence or absence in a document or across a text corpus. However, information about the neighborhood of the word, in terms of what words come after or before a word, wasn't taken into account in the approaches we have discussed so far. The neighborhood of a word carries important information in terms of what context the word is carrying in a sentence. The relationship between the word and its neighborhood tends to define the semantics of a word and its overall positioning and presence in a sentence. In this chapter, we will use this idea to build word vectors...