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Natural Language Processing with Java
Natural Language Processing with Java

Natural Language Processing with Java: Techniques for building machine learning and neural network models for NLP , Second Edition

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Natural Language Processing with Java

Finding Parts of Text

Finding parts of text is concerned with breaking text down into individual units, called tokens, and optionally performing additional processing on those tokens. This additional processing can include stemming, lemmatization, stopword removal, synonym expansion, and converting text to lowercase.

We will demonstrate several tokenization techniques found in the standard Java distribution. These are included because sometimes this is all you may need to do the job. There may be no need to import NLP libraries in this situation. However, these techniques are limited. This is followed by a discussion of specific tokenizers or tokenization approaches supported by NLP APIs. These examples will provide a reference for how the tokenizers are used and the type of output they produce. This is followed by a simple comparison of the differences between the approaches...

Understanding the parts of text

There are a number of ways to categorize parts of text. For example, we may be concerned with character-level issues, such as punctuation, with a possible need to ignore or expand contractions. At the word level, we may need to perform different operations, such as the following:

  • Identifying morphemes using stemming and/or lemmatization
  • Expanding abbreviations and acronyms
  • Isolating number units

We cannot always split words with punctuation, because the punctuation is sometimes considered to be part of the word, such as the word can't. We may also be concerned with grouping multiple words to form meaningful phrases. Sentence-detection can also be a factor. We do not necessarily want to group words that cross sentence boundaries.

In this chapter, we are primarily concerned with the tokenization process and a few specialized techniques, such...

What is tokenization?

Tokenization is the process of breaking text down into simpler units. For most text, we are concerned with isolating words. Tokens are split based on a set of delimiters. These delimiters are frequently whitespace characters. Whitespace in Java is defined by the Character class' isWhitespace method. These characters are listed in the following table. However, there may be a need, at times, to use a different set of delimiters. For example, different delimiters can be useful when whitespace delimiters obscure text breaks, such as paragraph boundaries, and detecting these text breaks is important:

Character

Meaning

Unicode space character

(space_separator, line_separator, or paragraph_separator)

\t

U+0009 horizontal tabulation

\n

U+000A line feed

\u000B

U+000B vertical tabulation

\f

U+000C form feed

\r

U+000D...

Simple Java tokenizers

There are several Java classes that support simple tokenization; some of them are as follows:

  • Scanner
  • String
  • BreakIterator
  • StreamTokenizer
  • StringTokenizer

Although these classes provide limited support, it is useful to understand how they can be used. For some tasks, these classes will suffice. Why use a more difficult-to-understand and less-efficient approach when a core Java class can do the job? We will cover each of these classes as they support the tokenization process.

The StreamTokenizer and StringTokenizer classes should not be used for new developments. Instead, the String class' split method is usually a better choice. They have been included here in case you come across them and wonder whether they should be used or not.

Using the Scanner class...

NLP tokenizer APIs

In this section, we will demonstrate several different tokenization techniques using the OpenNLP, Stanford, and LingPipe APIs. Although there are a number of other APIs available, we restricted the demonstration to these APIs. These examples will give you an idea of what techniques are available.

We will use a string called paragraph to illustrate these techniques. The string includes a new line break that may occur in real text in unexpected places. It is defined here:

private String paragraph = "Let's pause, \nand then +
+ "reflect.";

Using the OpenNLPTokenizer...

Understanding normalization

Normalization is a process that converts a list of words to a more uniform sequence. This is useful in preparing text for later processing. By transforming the words into a standard format, other operations are able to work with the data and will not have to deal with issues that might compromise the process. For example, converting all words to lowercase will simplify the searching process.

The normalization process can improve text-matching. For example, there are several ways that the term modem router can be expressed, such as modem and router, modem & router, modem/router, and modem-router. By normalizing these words to the common form, it makes it easier to supply the right information to a shopper.

Understand that the normalization process might also compromise an NLP task. Converting to lowercase letters can decrease the reliability of searches...

Summary

In this chapter, we illustrated various approaches to tokenizing text and performing normalization on text. We started with simple tokenization techniques based on core Java classes, such as the String class' split method and the StringTokenizer class. These approaches can be useful when we decide to forgo the use of the NLP API classes.

We demonstrated how tokenization can be performed using the OpenNLP, Stanford, and LingPipe APIs. We found variations in how tokenization can be performed and options that can be applied in these APIs. A brief comparison of their output was provided.

Normalization was discussed, which can involve converting characters to lowercase, expanding abbreviations, removing stopwords, stemming, and lemmatization. We illustrated how these techniques can be applied using both core Java classes and the NLP APIs.

In the next chapter, Chapter...

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Key benefits

  • Use deep learning and NLP techniques in Java to discover hidden insights in text
  • Work with popular Java libraries such as CoreNLP, OpenNLP, and Mallet
  • Explore machine translation, identifying parts of speech, and topic modeling

Description

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.

Who is this book for?

Natural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.

What you will learn

  • Understand basic NLP tasks and how they relate to one another
  • Discover and use the available tokenization engines
  • Apply search techniques to find people, as well as things, within a document
  • Construct solutions to identify parts of speech within sentences
  • Use parsers to extract relationships between elements of a document
  • Identify topics in a set of documents
  • Explore topic modeling from a document

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Publication date : Jul 31, 2018
Length: 318 pages
Edition : 2nd
Language : English
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Product Details

Publication date : Jul 31, 2018
Length: 318 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788993067
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Table of Contents

13 Chapters
Introduction to NLP Chevron down icon Chevron up icon
Finding Parts of Text Chevron down icon Chevron up icon
Finding Sentences Chevron down icon Chevron up icon
Finding People and Things Chevron down icon Chevron up icon
Detecting Part of Speech Chevron down icon Chevron up icon
Representing Text with Features Chevron down icon Chevron up icon
Information Retrieval Chevron down icon Chevron up icon
Classifying Texts and Documents Chevron down icon Chevron up icon
Topic Modeling Chevron down icon Chevron up icon
Using Parsers to Extract Relationships Chevron down icon Chevron up icon
Combined Pipeline Chevron down icon Chevron up icon
Creating a Chatbot Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
(3 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 100%
1 star 0%
Cliente Amazon May 15, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Libro banale non tecnico. Insegna solo ad usare delle librerie
Amazon Verified review Amazon
Daniel Oct 03, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This book explains about the process, but the examples aren’t as detailed. The Python NLP books are way better.
Amazon Verified review Amazon
Marc Lorent Dec 27, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Aucun des concepts utilisés dans le NLP n'est vraiment expliqué et le livre est ressemble plus â une javadoc des API mentionnées qu'à un véritable livre sur le sujet. A vrai dire il donne l'impression d'être une compilation d'extraits d'articles de Wikimedia.Le seul vrai intérêt de ce livre sont les liens Internet qu'il contient ...
Amazon Verified review Amazon
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