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Natural Language Processing with Java

You're reading from   Natural Language Processing with Java Techniques for building machine learning and neural network models for NLP

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Product type Paperback
Published in Jul 2018
Publisher
ISBN-13 9781788993494
Length 318 pages
Edition 2nd Edition
Languages
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to NLP FREE CHAPTER 2. Finding Parts of Text 3. Finding Sentences 4. Finding People and Things 5. Detecting Part of Speech 6. Representing Text with Features 7. Information Retrieval 8. Classifying Texts and Documents 9. Topic Modeling 10. Using Parsers to Extract Relationships 11. Combined Pipeline 12. Creating a Chatbot 13. Other Books You May Enjoy

Inverse document frequency


If we consider all the terms with the same importance for all the queries, it will not work for all queries. If the documents are related to ice, it is obvious that "ice" will be in almost all documents, probably with high frequency. Collection frequency and document frequency are two different terms that need to be explained. A collection contains many documents. The collection frequency (cf) shows the frequency of terms (t) in all documents in the collection, whereas the document frequency (df) shows the frequency of t in a single document. So the word "ice" will have a high collection frequency, as it is presumed to appear in all the documents in the collection. A simple idea is to reduce the weight of such terms if they have a high collection frequency. Inverse frequency is defined as follows:

Here, is the total number of documents in a collection. The idf of a frequent term is likely to be low, and that of a rare term will be high.

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