In this chapter, we covered how to find information from unstructured data using various techniques. We covered boolean retrieval, dictionaries and tolerant retrieval. We also covered wild card queries and how it is used. Spelling correction is covered in brief followed by vector space model and TF-IDF weighting and we end with evaluation of information retrieval. In next chapter, Chapter 8, Classifying Texts and Documents we will cover how to classify texts and documents.
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