Book Image

Hands-On Natural Language Processing with PyTorch 1.x

By : Thomas Dop
Book Image

Hands-On Natural Language Processing with PyTorch 1.x

By: Thomas Dop

Overview of this book

In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots. By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them.
Table of Contents (14 chapters)
1
Section 1: Essentials of PyTorch 1.x for NLP
7
Section 3: Real-World NLP Applications Using PyTorch 1.x

Uses of stemming and lemmatization

Stemming and lemmatization are both a form of NLP that can be used to extract information from text. This is known as text mining. Text mining tasks come in a variety of categories, including text clustering, categorization, summarizing documents, and sentiment analysis. Stemming and lemmatization can be used in conjunction with deep learning to solve some of these tasks, as we will see later in this book.

By performing preprocessing using stemming and lemmatization, coupled with the removal of stop words, we can better reduce our sentences to understand their core meaning. By removing words that do not significantly contribute to the meaning of the sentence and by reducing words to their roots or lemmas, we can efficiently analyze sentences within our deep learning frameworks. If we are able to reduce a 10-word sentence to five words consisting of multiple core lemmas rather than multiple variations of similar words, this means much less data...