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 Object formResult() {
Set brs = new HashSet();
Set urs = new HashSet();
// scan each rule / history pair
int ruleCount = 0;
for (Iterator pairI = rulePairs.keySet().iterator(); pairI.hasNext(); ) {
if (ruleCount % 100 == 0) {
System.err.println("Rules multiplied: " + ruleCount);
}
ruleCount++;
Pair rulePair = (Pair) pairI.next();
Rule baseRule = (Rule) rulePair.first;
String baseLabel = (String) ruleToLabel.get(baseRule);
List history = (List) rulePair.second;
double totalProb = 0;
for (int depth = 1; depth <= HISTORY_DEPTH() && depth <= history.size(); depth++) {
List subHistory = history.subList(0, depth);
double c_label = labelPairs.getCount(new Pair(baseLabel, subHistory));
double c_rule = rulePairs.getCount(new Pair(baseRule, subHistory));
// System.out.println("Multiplying out "+baseRule+" with history "+subHistory);
// System.out.println("Count of "+baseLabel+" with "+subHistory+" is "+c_label);
// System.out.println("Count of "+baseRule+" with "+subHistory+" is "+c_rule );
double prob = (1.0 / HISTORY_DEPTH()) * (c_rule) / (c_label);
totalProb += prob;
for (int childDepth = 0; childDepth <= Math.min(HISTORY_DEPTH() - 1, depth); childDepth++) {
Rule rule = specifyRule(baseRule, subHistory, childDepth);
rule.score = (float) Math.log(totalProb);
// System.out.println("Created "+rule+" with score "+rule.score);
if (rule instanceof UnaryRule) {
urs.add(rule);
} else {
brs.add(rule);
}
}
}
}
System.out.println("Total states: " + stateNumberer.total());
BinaryGrammar bg = new BinaryGrammar(stateNumberer.total());
UnaryGrammar ug = new UnaryGrammar(stateNumberer.total());
for (Iterator brI = brs.iterator(); brI.hasNext(); ) {
BinaryRule br = (BinaryRule) brI.next();
bg.addRule(br);
}
for (Iterator urI = urs.iterator(); urI.hasNext(); ) {
UnaryRule ur = (UnaryRule) urI.next();
ug.addRule(ur);
}
return new Pair(ug, bg);
}
public static double LexicalSimilarityScoreMax(
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 posTag1 = taggedWords1.get(i).tag();
String word2 = taggedWords2.get(j).word();
String posTag2 = taggedWords2.get(j).tag();
ArrayList<TaggedWord> newList1 = new ArrayList<TaggedWord>();
if (posTag1.length() >= 3 && posTag1.substring(0, 3).equals("NNP")) {
newList1.add(taggedWords1.get(i));
} else {
String[] words = word1.split(" ");
for (int k = 0; k < words.length; k++) newList1.add(new TaggedWord(words[k], posTag1));
}
ArrayList<TaggedWord> newList2 = new ArrayList<TaggedWord>();
if (posTag2.length() >= 3 && posTag2.substring(0, 3).equals("NNP")) {
newList2.add(taggedWords2.get(j));
} else {
String[] words = word2.split(" ");
for (int k = 0; k < words.length; k++) newList2.add(new TaggedWord(words[k], posTag2));
}
double edgeWeight = LexicalSimilarityScoreMin(newList1, newList2, discoRAM, lp);
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;
}
protected void tallyInternalNode(Tree lt, List parents) {
// form base rule
String label = lt.label().value();
Rule baseR = ltToRule(lt);
ruleToLabel.put(baseR, label);
// act on each history depth
for (int depth = 0, maxDepth = Math.min(HISTORY_DEPTH(), parents.size());
depth <= maxDepth;
depth++) {
List history = new ArrayList(parents.subList(0, depth));
// tally each history level / rewrite pair
rulePairs.incrementCount(new Pair(baseR, history), 1);
labelPairs.incrementCount(new Pair(label, history), 1);
}
}
public static double LexicalSimilarity2Level(
String sentence1, String sentence2, DISCOSimilarity discoRAM, LexicalizedParser lp) {
Tree parse1 = lp.apply(sentence1);
Tree parse2 = lp.apply(sentence2);
int phraseSizeLimit = 2;
ArrayList<ArrayList<TaggedWord>> phrasesList1 = getPhrases(parse1, phraseSizeLimit);
ArrayList<ArrayList<TaggedWord>> phrasesList2 = getPhrases(parse2, phraseSizeLimit);
int length1 = phrasesList1.size();
int length2 = phrasesList2.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++) {
double edgeWeight = 0;
ArrayList<TaggedWord> taggedWords1 = phrasesList1.get(i);
ArrayList<TaggedWord> taggedWords2 = phrasesList2.get(j);
// edgeWeight = LexicalSimilarityScore(taggedWords1, taggedWords2, discoRAM, lp)/5.0;
edgeWeight = BestWordMatchEdgeWeight(taggedWords1, taggedWords2, discoRAM);
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;
if (arrSize == 0) finalScore = 0;
else finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / arrSize * 5;
return finalScore;
}
public static double LexicalSimilarityScoreWordNet(
String sentence1, String sentence2, LeskWSD tm, LexicalizedParser lp, WordNetSimilarity ws) {
ArrayList<TaggedWord> taggedWordsPrev1 = Preprocess(StanfordParse(sentence1, lp));
ArrayList<TaggedWord> taggedWordsPrev2 = Preprocess(StanfordParse(sentence2, lp));
ArrayList<TaggedWord> taggedWords1 = new ArrayList<TaggedWord>();
ArrayList<TaggedWord> taggedWords2 = new ArrayList<TaggedWord>();
WordNetSense[] sensesPrev1 = tm.LeskJWI(sentence1);
WordNetSense[] sensesPrev2 = tm.LeskJWI(sentence2);
// System.out.println("Senses found!");
ArrayList<WordNetSense> senses1 = new ArrayList<WordNetSense>();
ArrayList<WordNetSense> senses2 = new ArrayList<WordNetSense>();
for (int i = 0; i < taggedWordsPrev1.size(); i++) {
String word = taggedWordsPrev1.get(i).word();
String posTag = taggedWordsPrev1.get(i).tag();
if (posTag.length() >= 2 && posTag.substring(0, 2).equals("NN")) {
taggedWords1.add(new TaggedWord(word, "NN"));
senses1.add(sensesPrev1[i]);
} else if (posTag.length() >= 2 && posTag.substring(0, 2).equals("VB")) {
taggedWords1.add(new TaggedWord(word, "VB"));
senses1.add(sensesPrev1[i]);
}
}
for (int i = 0; i < taggedWordsPrev2.size(); i++) {
String word = taggedWordsPrev2.get(i).word();
String posTag = taggedWordsPrev2.get(i).tag();
if (posTag.length() >= 2 && posTag.substring(0, 2).equals("NN")) {
taggedWords2.add(new TaggedWord(word, "NN"));
senses2.add(sensesPrev2[i]);
} else if (posTag.length() >= 2 && posTag.substring(0, 2).equals("VB")) {
taggedWords2.add(new TaggedWord(word, "VB"));
senses2.add(sensesPrev2[i]);
}
}
// 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 {
// edgeWeight = ws.wuPalmerSimilarity(senses1.get(i), senses2.get(j));
edgeWeight = ws.linSimilarity(senses1.get(i), senses2.get(j));
}
} 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;
if (arrSize == 0) finalScore = 0;
else finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / arrSize * 5;
return finalScore;
}
