KNBigramLanguageModel.java

package nlp.assignments.lm;


import java.util.ArrayList;
import java.util.Collection;
import java.util.List;

import nlp.langmodel.LanguageModel;
import nlp.util.Counter;
import nlp.util.CounterMap;

/**
 * A dummy language model -- uses empirical unigram counts, plus a single
 * ficticious count for unknown words.
 */
public class KNBigramLanguageModel implements LanguageModel {

  static final String START = "<S>";
  static final String STOP = "</S>";
  static final String UNKNOWN = "*UNKNOWN*";
  static final int cutOff = 5;
  
  static final double discount = 0.6 ;

  Counter<String> wordCounter = new Counter<String>();
  CounterMap<String, String> bigramCounter = new CounterMap<String, String>();
  Counter<String> probabilities = new Counter<String>();
  Counter<String> backoffs = new Counter<String>();
  Counter<String> discountedWordCounter = new Counter<String>();
  Counter<String> discountedBigramCounter = new Counter<String>();

  public double getBigramProbability(String previousWord, String word) 
  {
	  double bigramProb = 0 ;
	  double bigramCount = bigramCounter.getCount(previousWord, word);
	  double unigramCount = wordCounter.getCount(previousWord);
	  if ( unigramCount == 0 )
	  {
		  unigramCount = wordCounter.getCount(UNKNOWN) ;
	  }
		 
	  if (bigramCount > 0 )
	  {
		  bigramProb =  ((bigramCount - discount) /unigramCount) ;
	  }
	  else
	  {
		  double alpha = backoffs.getCount(previousWord);
		  if (alpha == 0) 
		  {
				if (probabilities.getCount(previousWord) == 0) 
				{
					alpha = 1.0;
				}
		  }
		
		  double unigramContextCount = bigramCounter.getCounter(previousWord).totalCount();
		  bigramProb = alpha * unigramContextCount ;
	  }
	  
	  return bigramProb ;
  }
  
  public double getBigramProbability1(String previousWord, String word) 
  {
    double bigramProbability = probabilities.getCount(previousWord + " " + word);
	 if (Double.isNaN(bigramProbability) || Double.isInfinite(bigramProbability) || bigramProbability < 0)
		 System.err.println("stop");

    if (bigramProbability != 0) return bigramProbability;
    
    double unigramProbability = probabilities.getCount(word);
    if (unigramProbability == 0) {
//    	System.out.println("UNKNOWN Word: " + word);
    	unigramProbability = probabilities.getCount(UNKNOWN);
    }
	 if (Double.isNaN(unigramProbability) || Double.isInfinite(unigramProbability) || unigramProbability < 0)
		 System.err.println("stop");

	double backoff = backoffs.getCount(previousWord); //backoffs == alpha
	if (backoff == 0) {
		if (probabilities.getCount(previousWord) == 0) backoff = 1.0;
	}
	return unigramProbability * backoff;
  }

  public double getSentenceProbability(List<String> sentence) {
    List<String> stoppedSentence = new ArrayList<String>(sentence);
    stoppedSentence.add(0, START);
    stoppedSentence.add(STOP);
    double probability = 1.0;
    String previousWord = stoppedSentence.get(0);
    for (int i = 1; i < stoppedSentence.size(); i++) {
      String word = stoppedSentence.get(i);
      probability *= getBigramProbability(previousWord, word);
      previousWord = word;
    }
    return probability;
  }

  String generateWord() {
    double sample = Math.random();
    double sum = 0.0;
    for (String word : wordCounter.keySet()) {
      sum += wordCounter.getCount(word);
      if (sum > sample) {
        return word;
      }
    }
    return UNKNOWN;
  }

  public List<String> generateSentence() {
    List<String> sentence = new ArrayList<String>();
    String word = generateWord();
    while (!word.equals(STOP)) {
      sentence.add(word);
      word = generateWord();
    }
    return sentence;
  }

  public KNBigramLanguageModel(Collection<List<String>> sentenceCollection) {
    for (List<String> sentence : sentenceCollection) {
      List<String> stoppedSentence = new ArrayList<String>(sentence);
      stoppedSentence.add(0, START);
      stoppedSentence.add(STOP);
      String previousWord = stoppedSentence.get(0);
  //    wordCounter.incrementCount(previousWord, 1.0);
      for (int i = 1; i < stoppedSentence.size(); i++) 
      {
    	String word = stoppedSentence.get(i);
        wordCounter.incrementCount(word, 1.0);
        discountedWordCounter.incrementCount(word, 1.0);
        discountedBigramCounter.incrementCount(previousWord + " " + word, 1.0);
        bigramCounter.incrementCount(previousWord, word, 1.0);
        previousWord = word;
      }
    }
    wordCounter.incrementCount(UNKNOWN, 1.0);
    
    normalizeDistributions();
  }

  private void normalizeDistributions() 
  {
	double[] unigramBuckets = new double[cutOff + 2];
	for (String word : wordCounter.keySet()) 
	{
		double count = wordCounter.getCount(word);
		if (count <= cutOff + 1) 
		{
			unigramBuckets[(int)count]++;
		}
	 }
	
	double[] bigramBuckets = new double[cutOff + 2];
	for (String previousWord : bigramCounter.keySet()) 
	{
		Counter<String> currentCounter = bigramCounter.getCounter(previousWord);
		for (String word : currentCounter.keySet()) 
		{
			double count = currentCounter.getCount(word);
			if (count <= cutOff + 1) 
			{
				bigramBuckets[(int)count]++;
			}
		}
	 }
	
	 double normalizer = 1.0 / wordCounter.totalCount();
	 double A = (cutOff + 1) * unigramBuckets[cutOff + 1] / unigramBuckets[1];
	 
	 for (String word : wordCounter.keySet()) 
	 {
		 double count = wordCounter.getCount(word);
		 if (count > cutOff) probabilities.setCount(word, count * normalizer);
		 else
		 {
			 double discountedCount = (count + 1)* unigramBuckets[(int)count + 1] / unigramBuckets[(int)count];
			 double probability = count * normalizer * (discountedCount / count - A) / (1 - A);
			 probabilities.setCount(word, probability);
			 if (Double.isNaN(probability) || Double.isInfinite(probability) || probability < 0)
				 System.err.println("stop");
		 }
	 }
	 probabilities.setCount(UNKNOWN, unigramBuckets[1] * normalizer / wordCounter.size());
	  
	 A = (cutOff + 1) * bigramBuckets[cutOff + 1] / bigramBuckets[1];
	 Counter<String> forwardProbability = new Counter<String>();
	 Counter<String> backwardProbability = new Counter<String>();
	 for (String previousWord : bigramCounter.keySet()) {
		 Counter<String> currentCounter = bigramCounter.getCounter(previousWord);
		 normalizer = 1.0 / currentCounter.totalCount();
		 double probability = 0;
		 double probabilitySoFar = 0;
		 for (String word : currentCounter.keySet()) {
			 double count = currentCounter.getCount(word);
			 if (count > cutOff) {
				 probability = count * normalizer;
//				 probability *= 0.99;
			 }
			 else {
				 double discountedCount = (count + 1) * bigramBuckets[(int)count + 1] / bigramBuckets[(int)count];
				 probability = count * normalizer * (discountedCount / count - A) / (1 - A);
			 }
			 if (Double.isNaN(probability) || Double.isInfinite(probability) || probability < 0)
				 System.err.println("stop");
			 probabilities.setCount(previousWord + " " + word, probability);
			 backwardProbability.incrementCount(previousWord, probabilities.getCount(word));
			 probabilitySoFar += probability;
		 }
		 forwardProbability.setCount(previousWord, probabilitySoFar);
	 }

	 for (String word : wordCounter.keySet()) {
		 double backoff = (1.0 - forwardProbability.getCount(word)) / (1.0 - backwardProbability.getCount(word));
		 if (Double.isNaN(backoff) || Double.isInfinite(backoff) || backoff == 0)
			 System.err.println("stop");
		 backoffs.setCount(word, backoff);
	 }
  }
}