In this chapter, we began with understanding RNNs and how they enable us to capture sequential dependencies in data. We made an effort to understand the problem of the RNN in terms of it not being able to capture long-term dependencies because of vanishing and exploding gradient issues. We also looked at various forms an RNN can take, depending on the type of problem it is being used to solve. We followed that up with a brief discussion on some variants of RNNs by talking about bidirectional and deep RNNs. We went a step further next and looked at how the vanishing and exploding gradient problem can be solved by adding memory to the network and, as a result, we had an expansive discussion on LSTM, which is a variant of an RNN, using the concept of a memory state. We tried to solve the problem of text generation, where we used LSTMs to generate text for describing hotels in the city of Mumbai. Finally, we had a brief discussion on other memory variants of an RNN, including GRUs...
Hands-On Python Natural Language Processing
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Hands-On Python Natural Language Processing
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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)
Preface
Section 1: Introduction
Free Chapter
Understanding the Basics of NLP
NLP Using Python
Section 2: Natural Language Representation and Mathematics
Building Your NLP Vocabulary
Transforming Text into Data Structures
Word Embeddings and Distance Measurements for Text
Exploring Sentence-, Document-, and Character-Level Embeddings
Section 3: NLP and Learning
Identifying Patterns in Text Using Machine Learning
From Human Neurons to Artificial Neurons for Understanding Text
Applying Convolutions to Text
Capturing Temporal Relationships in Text
State of the Art in NLP
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