In this chapter, we had a look at some of the recent advancements in the field of NLP, encompassing Seq2Seq modeling, the attention mechanism, the Transformer model, and BERT, all of which have revolutionized the way NLP problems are approached today. We began with a discussion on Seq2Seq modeling where we looked at its core components, the encoder and decoder. Based on the knowledge garnered, we built a French-to-English translator using the encoder-decoder stack. After that, we had a detailed discussion on the attention mechanism, which has allowed great parallelization leading to fast NLP training, and has also improved upon the results from the existing architectures. Next, we looked at Transformers and discussed every component inside the encoder-decoder stack of the Transformers. We also saw how the attention mechanism can be used as the core building block of such architectures, and can possibly provide a replacement for the existing RNN-based architectures. Finally, we...
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.
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Current Title:
Hands-On Python Natural Language Processing
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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