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Hands-On Natural Language Processing with Python

By : Shanmugamani, Arumugam, Byiringiro, Joshi, Muthuswamy

2.8 (4)

Buy this Book

Hands-On Natural Language Processing with Python

2.8 (4)

By: Shanmugamani, Arumugam, Byiringiro, Joshi, Muthuswamy

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Getting Started

Basic concepts and terminologies in NLP

Applications of NLP

Summary

Text Classification and POS Tagging Using NLTK

Installing NLTK and its modules

Text preprocessing and exploratory analysis

Exploratory analysis of text

POS tagging

Training a sentiment classifier for movie reviews

Training a bag-of-words classifier

Summary

Deep Learning and TensorFlow

Deep learning

TensorFlow

Summary

Semantic Embedding Using Shallow Models

Word vectors

From word to document embeddings

Sentence2vec

Doc2vec

Summary

Text Classification Using LSTM

Data for text classification

Topic modeling

Deep learning meta architecture for text classification

Identifying spam in YouTube video comments using RNNs

Classifying news articles by topic using a CNN

Transfer learning using GloVe embeddings

Multi-label classification

Summary

Searching and DeDuplicating Using CNNs

Data

Training the model

Summary

Named Entity Recognition Using Character LSTM

NER with deep learning

Summary

Text Generation and Summarization Using GRUs

Generating text using RNNs

Text summarization

Summary

Question-Answering and Chatbots Using Memory Networks

The Question-Answering task

Memory networks for Question-Answering

Extending memory networks for dialog modeling

Summary

Machine Translation Using the Attention-Based Model

Overview of machine translation

Summary

Speech Recognition Using DeepSpeech

Overview of speech recognition

Building an RNN model for speech recognition

Summary

Text-to-Speech Using Tacotron

Overview of text to speech

TTS in deep learning

Implementation of Tacotron with Keras

Summary

Deploying Trained Models

Increasing performance

TensorFlow Serving

Deploying in the cloud

Deploying on mobile devices

Summary

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Text Generation and Summarization Using GRUs

In this chapter, we will describe methods for generating and summarizing text using deep learning techniques. Text generation is the process of automatically generating text, based on context and scope, by using an input source text. Some applications involving text generation include automatic weather report generation, medical report generation, and translating a given representation of input text into multiple languages. Text summarization is a related technique, in which a summary is generated from a source text. Some example tasks include generating news, product reviews, and business report summaries.

In this chapter, the main focus will be on providing the reader with approaches to text generation and summarization using recurrent neural networks (RNNs). The following topics will be covered in this chapter: