Maximum entropy is a statistical classification technique. It takes various characteristics of a subject, such as the use of specialized words or the presence of whiskers in a picture, and assigns a weight to each characteristic. These weights are eventually added up and normalized to a value between 0 and 1, indicating the probability that the subject is of a particular kind. With a high enough level of confidence, we can conclude that the text is all about high-energy physics or that we have a picture of a cat.

If you're interested, you can find a more complete explanation of this technique at https://nadesnotes.wordpress.com/2016/09/05/natural-language-processing-nlp-fundamentals-maximum-entropy-maxent/. In this recipe, we will demonstrate the use of maximum entropy with the OpenNLP TokenizerME class.

To prepare, we need to do the following:

1. Create a new Maven project.
2. Download the en-token.bin file from http://opennlp.sourceforge.net/models-1.5/. Save it at the root directory of the project.
3. Add the following POM dependency to your project:

<dependency>
    <groupId>org.apache.opennlp</groupId>
    <artifactId>opennlp-tools</artifactId>
    <version>1.9.0</version>
</dependency>

Let's go through the following steps:

1. Add the following imports to the project:

import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.File;
import java.io.FileInputStream;
import java.io.InputStream;
import opennlp.tools.tokenize.Tokenizer;
import opennlp.tools.tokenize.TokenizerME;
import opennlp.tools.tokenize.TokenizerModel;

2. Next, add the following code to the main method. This sequence initializes the text to be processed and creates an input stream to read in the tokenization model. Modify the first argument of the File constructor to reflect the path to the model files:

String sampleText = 
    "In addition, the rook was moved too far to be effective.";
try (InputStream modelInputStream = new FileInputStream(  
        new File("...", "en-token.bin"))) {
    ...
} catch (FileNotFoundException e) {
    // Handle exception
} catch (IOException e) {   
    // Handle exception
}

3. Add the following code to the try block. It creates a tokenizer model and then the actual tokenizer:

TokenizerModel tokenizerModel = 
    new TokenizerModel(modelInputStream);
Tokenizer tokenizer = new TokenizerME(tokenizerModel);

4. Insert the following code sequence that uses the tokenize method to create a list of tokens and then display the tokens:

String tokenList[] = tokenizer.tokenize(sampleText);
for (String token : tokenList) {
    System.out.println(token);
}

5. Next, execute the program. You should get the following output:

In
addition
,
the
rook
was
moved
too
far
to
be
effective
.

The sampleText variable holds the test string. A try-with-resources block is used to automatically close the InputStream. The new File statement throws a FileNotFoundException, while the new TokenizerModel(modelInputStream) statement throws an IOException, both of which need to be handled.

An instance of the TokenizerModel class is created using the en-token.bin model. This model has been trained to recognize English text. An instance of the TokenizerME class represents the tokenizer where the tokenize method is executed against it using the sample text. This method returns an array of strings that are then displayed. Note that the comma and period are treated as separate tokens.

• The OpenNLP API documentation can be found at https://opennlp.apache.org/docs/1.9.0/apidocs/opennlp-tools/index.html