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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

N-grams


N-grams is a probabilistic model used for predicting the next word, text, or letter. It captures language in a statistical structure as machines are better at dealing with numbers instead of text. Many companies use this approach in spelling correction and suggestions, breaking words, or summarizing text. Let's try to understand it. N-grams are simply a sequence of words or letters, mostly words. Consider the sentence "This is n-gram model" It has four words or tokens, so it's a 4-gram; 3-grams from the same text will be "This is n-gram" and "is n-gram model". Two words are a bigram, and one word is a unigram. Let's try this using Java with OpenNLP:

        String sampletext = "This is n-gram model";
        System.out.println(sampletext);

        StringList tokens = new             StringList(WhitespaceTokenizer.INSTANCE.tokenize(sampletext));
        System.out.println("Tokens " + tokens);

        NGramModel nGramModel = new NGramModel();
        nGramModel.add(tokens,3,4); 
...
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