rapidrake

A fast version of the Rapid Automatic Keyword Extraction (RAKE) algorithm

Installation

Assuming you're using Maven, follow these two steps to use rakidrake in your Java project:

1. Include a dependency on rapidrake in your POM:

<dependency>
    <groupId>io.github.crew102</groupId>
    <artifactId>rapidrake</artifactId>
    <version>0.1.4</version>
</dependency>

2. Download the opennlp trained models for sentence detection and part-of-speech tagging. You can find these two models (trained on various languages) on opennlp's model page. For example, you could use the English versions of the sentence detection and POS-tagger models. You'll specify the file paths to these models when you instantiate a RakeAlgorithm object (see below for example).

Basic usage

import io.github.crew102.rapidrake.RakeAlgorithm;
import io.github.crew102.rapidrake.data.SmartWords;
import io.github.crew102.rapidrake.model.RakeParams;
import io.github.crew102.rapidrake.model.Result;

public class Example {

  public static void main(String[] args) throws java.io.IOException {
    
    // Create an object to hold algorithm parameters
    String[] stopWords = new SmartWords().getSmartWords(); 
    String[] stopPOS = {"VB", "VBD", "VBG", "VBN", "VBP", "VBZ"}; 
    int minWordChar = 1;
    boolean shouldStem = true;
    String phraseDelims = "[-,.?():;\"!/]"; 
    RakeParams params = new RakeParams(stopWords, stopPOS, minWordChar, shouldStem, phraseDelims);
    
    // Create a RakeAlgorithm object
    // You can use the RakeAlgorithm(RakeParams, POSTaggerME, SentenceDetectorME)
    // constructor instead of the one shown below if you want to pass in 
    // pre-initialized opennlp models.  
    String POStaggerURL = "model-bin/en-pos-maxent.bin"; // The path to your POS tagging model
    String SentDetectURL = "model-bin/en-sent.bin"; // The path to your sentence detection model
    RakeAlgorithm rakeAlg = new RakeAlgorithm(params, POStaggerURL, SentDetectURL);
    
    // Call the rake method
    String txt = "dogs are great, don't you agree? I love dogs, especially big dogs";
    Result result = rakeAlg.rake(txt);
    
    // Print the resulting keywords (not stemmed)
    System.out.println(result.distinct());
    
  }
}

// [dogs (1.33), great (1), big dogs (3.33)]

Learning more

You can learn more about how RAKE works and the various parameters you can set by visiting slowraker's website.