In Chapter 5, Word Embeddings and Distance Measurements for Text, we looked at how information related to the ordering of words, along with their semantics, can be taken into account when building embeddings to represent words. The idea of building embeddings will be extended in this chapter. We will explore techniques that will help us build embeddings for documents and sentences, as well as words based on their characters. We will start by looking into an algorithm called Doc2Vec, which, as the name suggests, provides document- or paragraph-level contextual embeddings. A sentence can essentially be treated as a paragraph, and embeddings for individual sentences can also be obtained using Doc2Vec. We will briefly discuss techniques such as Sent2Vec, which are focused on obtaining embeddings for sentences based on...
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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