Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
4 (1)
Book Image

Hands-On Python Natural Language Processing

4 (1)
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

Building a basic chatbot

We discussed chatbots as one of the important real-world applications of NLP in Chapter 1, Understanding the Basics of NLP. By now, we know enough to create a basic chatbot that could be trained using a predefined corpus and provide responses to queries using similarity concepts. In this section, we will create a chatbot using the concepts of vectorization and cosine similarity.

The most important requirement for building a chatbot is the corpus or text data on which the chatbot will be trained. The corpus should be relevant and exhaustive. If you are building a chatbot for the Human Resources (HR) department of your organization, you would typically need a corpus with all HR policies to train the bot and not a corpus containing presidential speeches. You would also need to ensure that the response time is acceptable and that the bot is not taking an inordinate amount of time to respond. The bot should also ideally seem human-like and have an acceptable accuracy...