CODEWITHSUNDEEP
javalucenesimilarity
Kasun
Kasun

Reputation: 336

Better way of calculating document Similarity using Lucene

I’m indexing a collection of documents using Lucene by specifying TermVector at indexing time. Then I retrieve terms and their frequencies by reading the index and calculating TF-IDF score vectors for each document. Then, using the TF-IDF vectors, I calculate pairwise cosine similarity between documents using Wikipedia's cosine similarity equation.

This is my problem: Say I have two identical documents “A” and “B” in this collection (A and B have more than 200 sentences). If I calculate pairwise cosine similarity between A and B it gives me cosine value=1 which is perfectly OK. But if I remove a single sentence from Doc “B”, it gives me cosine similarity value around 0.85 between these two documents. The documents are almost similar but cosine values are not. I understand the problem is with the equation that I’m using.

Is there better way / equation that I can use for calculating cosine similarity between documents?

Edited

This is how I calculate Cosine Similarity, doc1[] and doc2[] are TF-IDF vectors for corresponding document. the vector contains only the scores but not the words 

private double cosineSimBetweenTwoDocs(float doc1[], float doc2[]) {
    double temp;
    int doc1Len = doc1.length;
    int doc2Len = doc2.length;
    float numerator = 0;
    float temSumDoc1 = 0;
    float temSumDoc2 = 0;
    double equlideanNormOfDoc1 = 0;
    double equlideanNormOfDoc2 = 0;
    if (doc1Len > doc2Len) {
        for (int i = 0; i < doc2Len; i++) {
            numerator += doc1[i] * doc2[i];
            temSumDoc1 += doc1[i] * doc1[i];
            temSumDoc2 += doc2[i] * doc2[i];
        }
        equlideanNormOfDoc1=Math.sqrt(temSumDoc1);
         equlideanNormOfDoc2=Math.sqrt(temSumDoc2);
    } else {
        for (int i = 0; i < doc1Len; i++) {
            numerator += doc1[i] * doc2[i];
            temSumDoc1 += doc1[i] * doc1[i];
            temSumDoc2 += doc2[i] * doc2[i];
        }
         equlideanNormOfDoc1=Math.sqrt(temSumDoc1);
         equlideanNormOfDoc2=Math.sqrt(temSumDoc2);
    }

    temp = numerator / (equlideanNormOfDoc1 * equlideanNormOfDoc2);
    return temp;
}

Upvotes: 3

Views: 6583

Answers (1)

Helium
Helium

Reputation: 368

As I told you in my comment, I think you made a mistake somewhere. The vectors actually contain the <word,frequency> pairs, not words only. Therefore, when you delete the sentence, only the frequency of the corresponding words are subtracted by 1 (the words after are not shifted). Consider the following example:

Document a:

A B C A A B C. D D E A B. D A B C B A.

Document b:

A B C A A B C. D A B C B A.

Vector a:

A:6, B:5, C:3, D:3, E:1

Vector b:

A:5, B:4, C:3, D:1, E:0

Which result in the following similarity measure:

(6*5+5*4+3*3+3*1+1*0)/(Sqrt(6^2+5^2+3^2+3^2+1^2) Sqrt(5^2+4^2+3^2+1^2+0^2))=
62/(8.94427*7.14143)=
0.970648

Edit I think your source code is not working as well. Consider the following code which works fine with the above example:

import java.util.HashMap;
import java.util.Map;

public class DocumentVector {
    Map<String, Integer> wordMap = new HashMap<String, Integer>();

    public void incCount(String word) {
        Integer oldCount = wordMap.get(word);
        wordMap.put(word, oldCount == null ? 1 : oldCount + 1);
    }

    double getCosineSimilarityWith(DocumentVector otherVector) {
        double innerProduct = 0;
        for(String w: this.wordMap.keySet()) {
            innerProduct += this.getCount(w) * otherVector.getCount(w);
        }
        return innerProduct / (this.getNorm() * otherVector.getNorm());
    }

    double getNorm() {
        double sum = 0;
        for (Integer count : wordMap.values()) {
            sum += count * count;
        }
        return Math.sqrt(sum);
    }

    int getCount(String word) {
        return wordMap.containsKey(word) ? wordMap.get(word) : 0;
    }

    public static void main(String[] args) {
        String doc1 = "A B C A A B C. D D E A B. D A B C B A.";
        String doc2 = "A B C A A B C. D A B C B A.";

        DocumentVector v1 = new DocumentVector();
        for(String w:doc1.split("[^a-zA-Z]+")) {
            v1.incCount(w);
        }

        DocumentVector v2 = new DocumentVector();
        for(String w:doc2.split("[^a-zA-Z]+")) {
            v2.incCount(w);
        }

        System.out.println("Similarity = " + v1.getCosineSimilarityWith(v2));
    }

}

Upvotes: 5

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