java - what - n gramas de caracteres

Generación de N-gram a partir de una oración

Question

Cómo generar un n-gramo de una cadena como:

String Input="This is my car."

Quiero generar n-gram con esta entrada:

Input Ngram size = 3

La salida debería ser:

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

Da una idea en Java, cómo implementar eso o si hay alguna biblioteca disponible para él.

Estoy tratando de usar este NGramTokenizer pero está dando una secuencia de n-grams de caracteres y quiero n-grams de secuencia de palabras.

Answer 1

Creo que esto haría lo que quieras:

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 "a pedido" 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();
}
}

Answer 2

Está buscando ShingleFilter .

Actualización: el enlace apunta a la versión 3.0.2. Esta clase puede estar en un paquete diferente en la versión más nueva de Lucene.

Answer 3

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;
}

P.ej

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]

Answer 4

 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. secuencia de n-gramas de palabras que menor que el valor de corte ignorará el conjunto de resultados. La entrada es una lista de oraciones, luego se analiza utilizando una herramienta de OpenNLP

Answer 5

/**
*
* @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 [This, is, This is, my, is my, This is my, car, my car, is my car]

Answer 6

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;
}