/** Returns the features for the highest-score current parse with semantics that equal sem. */
public HashVector getFeats(Exp sem) {
HashVector result = new HashVector();
List<ParseResult> pr = findBestParses(allParses, sem);
for (ParseResult p : pr) {
p.getFeats(result);
}
if (pr.size() > 1) result.divideBy(pr.size());
return result;
}
private List<ParseResult> removeRepeats(List<ParseResult> all) {
System.out.println("----------------------- all.size equals to --------------" + all.size());
List<ParseResult> bestList = new LinkedList<ParseResult>();
for (int i = 0; i < all.size(); i++) {
ParseResult e_i = all.get(i);
boolean best = true;
for (int j = i + 1; j < all.size(); j++) {
ParseResult e_j = all.get(j);
if (e_i.getExp().equals(e_j.getExp()) && e_i.getScore() <= e_j.getScore()) {
best = false;
break;
}
}
if (best) bestList.add(e_i);
}
return bestList;
}
/**
* Return a parsed chart of the input, pruning each cell to pruneN entries.
*
* <p>Uses the CKY algorithm.
*/
public Chart parse(String input, Exp pruningSem, boolean computeInside) {
List tokens = tokenize(input);
Globals.tokens = tokens;
Globals.lastWordIndex = tokens.size() - 1;
// create a chart and add the input words
Chart chart = new Chart(tokens);
if (pruningSem != null) {
chart.setPruningSem(pruningSem);
}
// initialize cells list with the lexical rule
lexicon.addCells(chart);
if (tempLex != null) {
tempLex.addCells(chart);
}
int size = tokens.size();
List temp = new LinkedList();
// now do the CKY parsing:
for (int len = 1; len < size; len++) {
for (int begin = 0; begin < size - len; begin++) {
for (int split = 0; split < len; split++) {
temp.clear();
Iterator leftIter = chart.getCellsIterator(begin, begin + split);
while (leftIter.hasNext()) {
Cell left = (Cell) leftIter.next();
Iterator rightIter = chart.getCellsIterator(begin + split + 1, begin + len);
while (rightIter.hasNext()) {
Cell right = (Cell) rightIter.next();
Iterator rules = binaryRules.iterator();
while (rules.hasNext()) {
((BinaryParseRule) rules.next()).newCellsFrom(left, right, temp);
}
}
}
chart.addAll(temp);
}
}
}
// System.out.println("fffffffffffffffff");
// for(ParseResult pr : chart.getParseResults()){
// System.out.println(pr + "#" + pr.getScore());
// }
// System.out.println("fffffffffffffffff");
chart.pruneParseResults(pruneN);
// store the parse results
allParses = chart.getParseResults();
allParses = removeRepeats(allParses);
// find best parses for test and for generating
// new lexical items.
bestParses = findBestParses(allParses);
// System.out.println("&&&&&&&&&&&&&&&&&&&&&");
// System.out.println(input + " " + pruningSem);
// for (int len = size-1; len>=0; len--){
// for (int begin = 0; begin<size-len; begin++){
// Iterator i = chart.getCellsIterator(begin,begin+len);
// while (i.hasNext()){
// Cell c = (Cell)i.next();
// System.out.println("len = " + len + " begin = " + begin + "\n" + c.lex);
// }
// }
// }
// System.out.println("&&&&&&&&&&&&&&&&&&&&&");
return chart;
}
public ParseResult bestParse(Exp sem) {
List<ParseResult> result = findBestParses(allParses, sem);
if (result.size() > 0) return result.get(0);
return null;
}
/**
* Expands the lexicon and updates the weight vector and feature indices. Does not add entries
* that are already present.
*/
public void addLexEntries(List lex) {
for (int i = 0; i < lex.size(); i++) {
addLexEntry((LexEntry) lex.get(i));
}
}