java:从句子中产生N-gram

您正在尋找ShingleFilter。

更新：鏈接指向版本3.0.2.此類可能在更高版本的Lucene中使用不同的軟體包。

我相信這会满足您的要求：

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();
        }
    }
}

Output:

This
is
my
car.
This is
is my
my car.
This is my
is my car.

---

實現為迭代器的"按需"解決方案：

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();
    }
}

此代碼返迴给定长度的所有字元串的陣列：

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;
}

例如

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;
}

Call:

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());

Output:

My time = 1 ms with ngramsize = 9 [This, is, This is, my, is my, This is my, car, my car, is my car]

pre

   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());
    }
}

這是我建立n-gram的代碼.在這種情况下，n = 2、3。小於截止值的n-gram單詞序列將从結果集中忽略.輸入的是句子列表，然後使用OpenNLP工具进行解析