public static double LexicalSimilarityScoreMin(
ArrayList<TaggedWord> taggedWords1,
ArrayList<TaggedWord> taggedWords2,
DISCOSimilarity discoRAM,
LexicalizedParser lp) {
// System.out.println(taggedWords1.size() + "," + taggedWords2.size());
// array of edge weights with default weight 0
int length1 = taggedWords1.size();
int length2 = taggedWords2.size();
int arrSize = Math.max(length1, length2);
double[][] array = new double[arrSize][arrSize];
for (int i = 0; i < arrSize; i++) {
for (int j = 0; j < arrSize; j++) {
array[i][j] = 0;
}
}
for (int i = 0; i < length1; i++) {
for (int j = 0; j < length2; j++) {
String word1 = taggedWords1.get(i).word();
String word2 = taggedWords2.get(j).word();
double edgeWeight = 0;
// LSA Similarity
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
// DISCO Similarity
// DISCOSimilarity discoObj = new DISCOSimilarity();
try {
if (word1.compareToIgnoreCase(word2) == 0) edgeWeight = 1;
else {
edgeWeight = discoRAM.similarity2(word1, word2);
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
}
} catch (Exception ex) {
ex.printStackTrace();
}
array[i][j] = edgeWeight;
}
}
// System.out.println("Hungarian starts " + arrSize);
double finalScore;
String sumType = "max";
int minLength = Math.min(length1, length2);
finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / minLength * 5;
// finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType)/arrSize * 5;
return finalScore;
}
public static double BestWordMatchEdgeWeight(
ArrayList<TaggedWord> taggedWords1,
ArrayList<TaggedWord> taggedWords2,
DISCOSimilarity discoRAM) {
double bestMatchScore = 0;
for (int i = 0; i < taggedWords1.size(); i++) {
String word1 = taggedWords1.get(i).word();
for (int j = 0; j < taggedWords2.size(); j++) {
String word2 = taggedWords2.get(j).word();
double currentScore;
if (word1.equals(word2)) currentScore = 1;
else currentScore = discoRAM.similarity2(word1, word2);
if (currentScore > bestMatchScore) bestMatchScore = currentScore;
}
}
return bestMatchScore;
}
public static double LexicalSimilarityScoreDISCOWordNet(
String sentence1,
String sentence2,
DISCOSimilarity discoRAM,
LexicalizedParser lp,
LeskWSD tm,
WordNetSimilarity ws) {
ArrayList<TaggedWord> taggedWords1 = Preprocess(StanfordParse(sentence1, lp));
ArrayList<TaggedWord> taggedWords2 = Preprocess(StanfordParse(sentence2, lp));
WordNetSense[] sensesPrev1 = tm.LeskJWI(sentence1);
WordNetSense[] sensesPrev2 = tm.LeskJWI(sentence2);
// System.out.println(taggedWords1.size() + "," + taggedWords2.size());
// array of edge weights with default weight 0
int length1 = taggedWords1.size();
int length2 = taggedWords2.size();
int arrSize = Math.max(length1, length2);
double[][] array = new double[arrSize][arrSize];
for (int i = 0; i < arrSize; i++) {
for (int j = 0; j < arrSize; j++) {
array[i][j] = 0;
}
}
for (int i = 0; i < length1; i++) {
for (int j = 0; j < length2; j++) {
String word1 = taggedWords1.get(i).word();
String posTag1 = taggedWords1.get(i).tag();
String word2 = taggedWords2.get(j).word();
String posTag2 = taggedWords2.get(j).tag();
double edgeWeight = 0;
// LSA Similarity
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
// DISCO Similarity
// DISCOSimilarity discoObj = new DISCOSimilarity();
try {
if (word1.compareToIgnoreCase(word2) == 0) edgeWeight = 1;
else if (posTag1.length() > 1
&& posTag1.substring(0, 2).equals("NN")
&& posTag2.length() > 1
&& posTag2.substring(0, 2).equals("NN")) {
edgeWeight = ws.linSimilarity(sensesPrev1[i], sensesPrev2[j]);
} else if (posTag1.length() > 1
&& posTag1.substring(0, 2).equals("VB")
&& posTag2.length() > 1
&& posTag2.substring(0, 2).equals("VB")) {
edgeWeight = ws.linSimilarity(sensesPrev1[i], sensesPrev2[j]);
} else {
edgeWeight = discoRAM.similarity2(word1, word2);
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
}
} catch (Exception ex) {
ex.printStackTrace();
}
array[i][j] = edgeWeight;
}
}
// System.out.println("Hungarian starts " + arrSize);
double finalScore;
String sumType = "max";
int minLength = Math.min(length1, length2);
// finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType)/minLength * 5;
finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / arrSize * 5;
return finalScore;
}
