public Tree<String> getBestParseOld(List<String> sentence) {
// TODO: This implements the CKY algorithm
CounterMap<String, String> parseScores = new CounterMap<String, String>();
System.out.println(sentence.toString());
// First deal with the lexicons
int index = 0;
int span = 1; // All spans are 1 at the lexicon level
for (String word : sentence) {
for (String tag : lexicon.getAllTags()) {
double score = lexicon.scoreTagging(word, tag);
if (score >= 0.0) { // This lexicon may generate this word
// We use a counter map in order to store the scores for this sentence parse.
parseScores.setCount(index + " " + (index + span), tag, score);
}
}
index = index + 1;
}
// handle unary rules now
HashMap<String, Triplet<Integer, String, String>> backHash =
new HashMap<
String, Triplet<Integer, String, String>>(); // hashmap to store back propation
// System.out.println("Lexicons found");
Boolean added = true;
while (added) {
added = false;
for (index = 0; index < sentence.size(); index++) {
// For each index+ span pair, get the counter.
Counter<String> count = parseScores.getCounter(index + " " + (index + span));
PriorityQueue<String> countAsPQ = count.asPriorityQueue();
while (countAsPQ.hasNext()) {
String entry = countAsPQ.next();
// System.out.println("I am fine here!!");
List<UnaryRule> unaryRules = grammar.getUnaryRulesByChild(entry);
for (UnaryRule rule : unaryRules) {
// These are the unary rules which might give rise to the above preterminal
double prob =
rule.getScore() * parseScores.getCount(index + " " + (index + span), entry);
if (prob > parseScores.getCount(index + " " + (index + span), rule.parent)) {
parseScores.setCount(index + " " + (index + span), rule.parent, prob);
backHash.put(
index + " " + (index + span) + " " + rule.parent,
new Triplet<Integer, String, String>(-1, entry, null));
added = true;
}
}
}
}
}
// System.out.println("Lexicon unaries dealt with");
// Now work with the grammar to produce higher level probabilities
for (span = 2; span <= sentence.size(); span++) {
for (int begin = 0; begin <= (sentence.size() - span); begin++) {
int end = begin + span;
for (int split = begin + 1; split <= end - 1; split++) {
Counter<String> countLeft = parseScores.getCounter(begin + " " + split);
Counter<String> countRight = parseScores.getCounter(split + " " + end);
// List<BinaryRule> leftRules= new ArrayList<BinaryRule>();
HashMap<Integer, BinaryRule> leftMap = new HashMap<Integer, BinaryRule>();
// List<BinaryRule> rightRules=new ArrayList<BinaryRule>();
HashMap<Integer, BinaryRule> rightMap = new HashMap<Integer, BinaryRule>();
for (String entry : countLeft.keySet()) {
for (BinaryRule rule : grammar.getBinaryRulesByLeftChild(entry)) {
if (!leftMap.containsKey(rule.hashCode())) {
leftMap.put(rule.hashCode(), rule);
}
}
}
for (String entry : countRight.keySet()) {
for (BinaryRule rule : grammar.getBinaryRulesByRightChild(entry)) {
if (!rightMap.containsKey(rule.hashCode())) {
rightMap.put(rule.hashCode(), rule);
}
}
}
// System.out.println("About to enter the rules loops");
for (Integer ruleHash : leftMap.keySet()) {
if (rightMap.containsKey(ruleHash)) {
BinaryRule ruleRight = rightMap.get(ruleHash);
double prob =
ruleRight.getScore()
* parseScores.getCount(begin + " " + split, ruleRight.leftChild)
* parseScores.getCount(split + " " + end, ruleRight.rightChild);
// System.out.println(begin+" "+ end +" "+ ruleRight.parent+ " "+ prob);
if (prob > parseScores.getCount(begin + " " + end, ruleRight.parent)) {
// System.out.println(begin+" "+ end +" "+ ruleRight.parent+ " "+ prob);
// System.out.println("parentrule :"+ ruleRight.getParent());
parseScores.setCount(begin + " " + end, ruleRight.getParent(), prob);
backHash.put(
begin + " " + end + " " + ruleRight.parent,
new Triplet<Integer, String, String>(
split, ruleRight.leftChild, ruleRight.rightChild));
}
}
}
// System.out.println("Exited rules loop");
}
// System.out.println("Grammar found for " + begin + " "+ end);
// Now handle unary rules
added = true;
while (added) {
added = false;
Counter<String> count = parseScores.getCounter(begin + " " + end);
PriorityQueue<String> countAsPriorityQueue = count.asPriorityQueue();
while (countAsPriorityQueue.hasNext()) {
String entry = countAsPriorityQueue.next();
List<UnaryRule> unaryRules = grammar.getUnaryRulesByChild(entry);
for (UnaryRule rule : unaryRules) {
double prob = rule.getScore() * parseScores.getCount(begin + " " + (end), entry);
if (prob > parseScores.getCount(begin + " " + (end), rule.parent)) {
parseScores.setCount(begin + " " + (end), rule.parent, prob);
backHash.put(
begin + " " + (end) + " " + rule.parent,
new Triplet<Integer, String, String>(-1, entry, null));
added = true;
}
}
}
}
// System.out.println("Unaries dealt for " + begin + " "+ end);
}
}
// Create and return the parse tree
Tree<String> parseTree = new Tree<String>("null");
// System.out.println(parseScores.getCounter(0+" "+sentence.size()).toString());
String parent = parseScores.getCounter(0 + " " + sentence.size()).argMax();
if (parent == null) {
System.out.println(parseScores.getCounter(0 + " " + sentence.size()).toString());
System.out.println("THIS IS WEIRD");
}
parent = "ROOT";
parseTree = getParseTreeOld(sentence, backHash, 0, sentence.size(), parent);
// System.out.println("PARSE SCORES");
// System.out.println(parseScores.toString());
// System.out.println("BACK HASH");
// System.out.println(backHash.toString());
// parseTree = addRoot(parseTree);
// System.out.println(parseTree.toString());
// return parseTree;
return TreeAnnotations.unAnnotateTree(parseTree);
}
public void train(List<SentencePair> trainingPairs) {
sourceTargetCounts = new CounterMap<String, String>();
sourceTargetDistortions = new CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>>();
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
targetSentence.add(WordAligner.NULL_WORD);
int m = sourceSentence.size();
int l = targetSentence.size();
for (int i = 0; i < m; i++) {
String sourceWord = sourceSentence.get(i);
for (int j = 0; j < l; j++) {
String targetWord = targetSentence.get(j);
sourceTargetCounts.setCount(sourceWord, targetWord, 1.0);
Pair<Integer, Integer> lmPair = new Pair<Integer, Integer>(l, m);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
sourceTargetDistortions.setCount(jiPair, lmPair, 1.0);
}
}
}
// Use Model 1 to train params
double delta = Double.POSITIVE_INFINITY;
for (int i = 0; i < MAX_ITERS && delta > CONVERGENCE; i++) {
CounterMap<String, String> tempSourceTargetCounts = new CounterMap<String, String>();
Counter<String> targetCounts = new Counter<String>();
delta = 0.0;
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
Counter<String> sourceTotals = new Counter<String>();
for (String sourceWord : sourceSentence) {
for (String targetWord : targetSentence) {
sourceTotals.incrementCount(
sourceWord, sourceTargetCounts.getCount(sourceWord, targetWord));
}
}
for (String sourceWord : sourceSentence) {
for (String targetWord : targetSentence) {
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double sourceTotal = sourceTotals.getCount(sourceWord);
tempSourceTargetCounts.incrementCount(sourceWord, targetWord, transProb / sourceTotal);
targetCounts.incrementCount(targetWord, transProb / sourceTotal);
}
}
}
// update t(s|t) values
for (String sourceWord : tempSourceTargetCounts.keySet()) {
for (String targetWord : tempSourceTargetCounts.getCounter(sourceWord).keySet()) {
double oldProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double newProb =
tempSourceTargetCounts.getCount(sourceWord, targetWord)
/ targetCounts.getCount(targetWord);
sourceTargetCounts.setCount(sourceWord, targetWord, newProb);
delta += Math.pow(oldProb - newProb, 2.0);
}
}
delta /= sourceTargetCounts.totalSize();
}
// Maximizing for ibm model 2
delta = Double.POSITIVE_INFINITY;
for (int iter = 0; iter < MAX_ITERS && delta > CONVERGENCE; iter++) {
CounterMap<String, String> tempSourceTargetCounts = new CounterMap<String, String>();
CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>> tempSourceTargetDistortions =
new CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>>();
Counter<String> targetCounts = new Counter<String>();
CounterMap<Pair<Integer, Integer>, Integer> targetDistorts =
new CounterMap<Pair<Integer, Integer>, Integer>();
delta = 0.0;
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
CounterMap<Pair<Integer, Integer>, Integer> distortSourceTotals =
new CounterMap<Pair<Integer, Integer>, Integer>();
Pair<Integer, Integer> lmPair =
new Pair<Integer, Integer>(targetSentence.size(), sourceSentence.size());
for (int i = 0; i < sourceSentence.size(); i++) {
String sourceWord = sourceSentence.get(i);
for (int j = 0; j < targetSentence.size(); j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double currTransProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double currAlignProb = sourceTargetDistortions.getCount(jiPair, lmPair);
distortSourceTotals.incrementCount(lmPair, i, currTransProb * currAlignProb);
}
}
for (int i = 0; i < sourceSentence.size(); i++) {
String sourceWord = sourceSentence.get(i);
double distortTransSourceTotal = distortSourceTotals.getCount(lmPair, i);
for (int j = 0; j < targetSentence.size(); j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double distortProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double update =
(transProb * distortProb) / (distortTransSourceTotal); // q(j|ilm)t(f|e)/totals
tempSourceTargetCounts.incrementCount(sourceWord, targetWord, update);
tempSourceTargetDistortions.incrementCount(jiPair, lmPair, update);
targetCounts.incrementCount(targetWord, update);
targetDistorts.incrementCount(lmPair, i, update);
}
}
}
// update t(s|t) values
double delta_trans = 0.0;
for (String sourceWord : tempSourceTargetCounts.keySet()) {
for (String targetWord : tempSourceTargetCounts.getCounter(sourceWord).keySet()) {
double oldProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double newProb =
tempSourceTargetCounts.getCount(sourceWord, targetWord)
/ targetCounts.getCount(targetWord);
sourceTargetCounts.setCount(sourceWord, targetWord, newProb);
delta += Math.pow(oldProb - newProb, 2.0);
}
}
// update q(j|ilm) values
double delta_dist = 0.0;
for (Pair<Integer, Integer> jiPair : tempSourceTargetDistortions.keySet()) {
for (Pair<Integer, Integer> lmPair :
tempSourceTargetDistortions.getCounter(jiPair).keySet()) {
double oldProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double tempAlignProb = tempSourceTargetDistortions.getCount(jiPair, lmPair);
double tempTargetDist = targetDistorts.getCount(lmPair, jiPair.getSecond());
double newProb = tempAlignProb / tempTargetDist;
sourceTargetDistortions.setCount(jiPair, lmPair, newProb);
delta_dist += Math.pow(oldProb - newProb, 2.0);
}
}
delta =
(delta_trans / sourceTargetCounts.totalSize()
+ delta_dist / sourceTargetDistortions.totalSize())
/ 2.0;
}
}