In this chapter, we covered word embedding and why it is important in natural language processing. N-grams were used to show how the words are treated as a vector and how the count of words are stored to find the relevance. GloVe and word2vec are two common approaches to word embedding, where the word counts or probabilities are stored in vectors. Both of these approaches lead to high dimensionality, which is not feasible to process in the real world, especially on mobile devices or devices with less memory. We have seen two different approaches to reduce the dimensionality. In next chapter, Chapter 7, Information Retrieval we will see how information retrieval can be done from the unstructured format such as text.
Natural Language Processing with Java - Second Edition
By :
Natural Language Processing with Java - Second Edition
By:
Overview of this book
Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes.
You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more.
By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.
Table of Contents (19 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Free Chapter
Introduction to NLP
Finding Parts of Text
Finding Sentences
Finding People and Things
Detecting Part of Speech
Representing Text with Features
Information Retrieval
Classifying Texts and Documents
Topic Modeling
Using Parsers to Extract Relationships
Combined Pipeline
Creating a Chatbot
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Index
Customer Reviews