Book Image

Hands-On Natural Language Processing with Python

By : Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy
Book Image

Hands-On Natural Language Processing with Python

By: Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy

Overview of this book

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.
Table of Contents (15 chapters)
6
Searching and DeDuplicating Using CNNs
7
Named Entity Recognition Using Character LSTM

Text-to-Speech Using Tacotron

Text-to-speech (TTS) is the act of converting text into intelligible and natural speech. Before we delve into deep learning approaches to handle TTS, we should ask ourselves the following questions: what are TTS systems for? And why do we need them in the first place?

Well, there are many use cases for TTS. One of the most obvious is that it allows blind people to listen to written content. Indeed, Braille-based books, devices, or signs are not always available, and blind people can't always have someone read to them. In the near future, there might be smart glasses that can describe the surrounding environment and read urban signs and text-based indications to their users.

Many people struggle from childhood with learning disabilities like dyslexia. Robust TTS systems can help them on a daily basis, increasing their productivity at school or...