Generación de N-gramo a partir de una oración

Creo que esto haría lo que quieres:

import java.util.*;

public class Test {

    public static List<String> ngrams(int n, String str) {
        List<String> ngrams = new ArrayList<String>();
        String[] words = str.split(" ");
        for (int i = 0; i < words.length - n + 1; i++)
            ngrams.add(concat(words, i, i+n));
        return ngrams;
    }

    public static String concat(String[] words, int start, int end) {
        StringBuilder sb = new StringBuilder();
        for (int i = start; i < end; i++)
            sb.append((i > start ? " " : "") + words[i]);
        return sb.toString();
    }

    public static void main(String[] args) {
        for (int n = 1; n <= 3; n++) {
            for (String ngram : ngrams(n, "This is my car."))
                System.out.println(ngram);
            System.out.println();
        }
    }
}

Salida:

This
is
my
car.

This is
is my
my car.

This is my
is my car.

Una solución" bajo demanda " implementada como un iterador:

class NgramIterator implements Iterator<String> {

    String[] words;
    int pos = 0, n;

    public NgramIterator(int n, String str) {
        this.n = n;
        words = str.split(" ");
    }

    public boolean hasNext() {
        return pos < words.length - n + 1;
    }

    public String next() {
        StringBuilder sb = new StringBuilder();
        for (int i = pos; i < pos + n; i++)
            sb.append((i > pos ? " " : "") + words[i]);
        pos++;
        return sb.toString();
    }

    public void remove() {
        throw new UnsupportedOperationException();
    }
}

Este código devuelve una matriz de todas las cadenas de la longitud dada:

public static String[] ngrams(String s, int len) {
    String[] parts = s.split(" ");
    String[] result = new String[parts.length - len + 1];
    for(int i = 0; i < parts.length - len + 1; i++) {
       StringBuilder sb = new StringBuilder();
       for(int k = 0; k < len; k++) {
           if(k > 0) sb.append(' ');
           sb.append(parts[i+k]);
       }
       result[i] = sb.toString();
    }
    return result;
}

Por ejemplo

System.out.println(Arrays.toString(ngrams("This is my car", 2)));
//--> [This is, is my, my car]
System.out.println(Arrays.toString(ngrams("This is my car", 3)));
//--> [This is my, is my car] 

/**
 * 
 * @param sentence should has at least one string
 * @param maxGramSize should be 1 at least
 * @return set of continuous word n-grams up to maxGramSize from the sentence
 */
public static List<String> generateNgramsUpto(String str, int maxGramSize) {

    List<String> sentence = Arrays.asList(str.split("[\\W+]"));

    List<String> ngrams = new ArrayList<String>();
    int ngramSize = 0;
    StringBuilder sb = null;

    //sentence becomes ngrams
    for (ListIterator<String> it = sentence.listIterator(); it.hasNext();) {
        String word = (String) it.next();

        //1- add the word itself
        sb = new StringBuilder(word);
        ngrams.add(word);
        ngramSize=1;
        it.previous();

        //2- insert prevs of the word and add those too
        while(it.hasPrevious() && ngramSize<maxGramSize){
            sb.insert(0,' ');
            sb.insert(0,it.previous());
            ngrams.add(sb.toString());
            ngramSize++;
        }

        //go back to initial position
        while(ngramSize>0){
            ngramSize--;
            it.next();
        }                   
    }
    return ngrams;
}

Llamada:

long startTime = System.currentTimeMillis();
ngrams = ToolSet.generateNgramsUpto("This is my car.", 3);
long stopTime = System.currentTimeMillis();
System.out.println("My time = "+(stopTime-startTime)+" ms with ngramsize = "+ngrams.size());
System.out.println(ngrams.toString());

Salida:

Mi tiempo = 1 ms con ngramsize = 9 [Esto, es, Esto es, mi, es mi, Esto es mi coche, mi coche, mi coche]

    public static void CreateNgram(ArrayList<String> list, int cutoff) {
    try
    {
        NGramModel ngramModel = new NGramModel();
        POSModel model = new POSModelLoader().load(new File("en-pos-maxent.bin"));
        PerformanceMonitor perfMon = new PerformanceMonitor(System.err, "sent");
        POSTaggerME tagger = new POSTaggerME(model);
        perfMon.start();
        for(int i = 0; i<list.size(); i++)
        {
            String inputString = list.get(i);
            ObjectStream<String> lineStream = new PlainTextByLineStream(new StringReader(inputString));
            String line;
            while ((line = lineStream.read()) != null) 
            {
                String whitespaceTokenizerLine[] = WhitespaceTokenizer.INSTANCE.tokenize(line);
                String[] tags = tagger.tag(whitespaceTokenizerLine);

                POSSample sample = new POSSample(whitespaceTokenizerLine, tags);

                perfMon.incrementCounter();

                String words[] = sample.getSentence();

                if(words.length > 0)
                {
                    for(int k = 2; k< 4; k++)
                    {
                        ngramModel.add(new StringList(words), k, k);
                    }
                }
            }
        }
        ngramModel.cutoff(cutoff, Integer.MAX_VALUE);
        Iterator<StringList> it = ngramModel.iterator();
        while(it.hasNext())
        {
            StringList strList = it.next();
            System.out.println(strList.toString());
        }
        perfMon.stopAndPrintFinalResult();
    }catch(Exception e)
    {
        System.out.println(e.toString());
    }
}

Aquí están mis códigos para crear n-gram. En este caso, n = 2, 3. n-gramo de secuencia de palabras que menor que el valor de corte ignorará del conjunto de resultados. La entrada es una lista de oraciones, luego se analiza usando una herramienta de OpenNLP

public static void main(String[] args) {

    String[] words = "This is my car.".split(" ");
    for (int n = 0; n < 3; n++) {

        List<String> list = ngrams(n, words);
        for (String ngram : list) {
            System.out.println(ngram);
        }
        System.out.println();

    }
}

public static List<String> ngrams(int stepSize, String[] words) {
    List<String> ngrams = new ArrayList<String>();
    for (int i = 0; i < words.length-stepSize; i++) {

        String initialWord = "";
        int internalCount = i;
        int internalStepSize = i + stepSize;
        while (internalCount <= internalStepSize
                && internalCount < words.length) {
            initialWord = initialWord+" " + words[internalCount];
            ++internalCount;
        }
        ngrams.add(initialWord);

    }
    return ngrams;
}