public Event getHistory(TrainerEvent trainerEvent, int backOffLevel) {
ModifierEvent modEvent = (ModifierEvent) trainerEvent;
if (Language.treebank().isBaseNP(modEvent.parent()))
return getBaseNPHistory(modEvent, backOffLevel);
Symbol side = Constants.sideToSym(modEvent.side());
MutableEvent hist = historiesWithSubcats[backOffLevel];
hist.clear();
Symbol verbInterveningSym = Constants.booleanToSym(modEvent.verbIntervening());
Symbol mappedPrevModSym = NTMapper.map(modEvent.previousMods().symbolAt(0));
Symbol parent = Language.training().removeArgAugmentation(modEvent.parent());
switch (backOffLevel) {
case 0:
// for p(M(t)_i | P, H, w, t, verbIntervening, map(M_i-1), subcat, side)
hist.add(0, parent);
hist.add(0, Language.training().removeGapAugmentation(modEvent.head()));
hist.add(0, modEvent.headWord().word());
hist.add(0, modEvent.headWord().tag());
hist.add(0, verbInterveningSym);
hist.add(0, mappedPrevModSym);
hist.add(1, modEvent.subcat());
hist.add(0, side);
break;
case 1:
// for p(M(t)_i | P, H, t, verbIntervening, map(M_i-1), subcat, side)
hist.add(0, parent);
hist.add(0, Language.training().removeGapAugmentation(modEvent.head()));
hist.add(0, modEvent.headWord().tag());
hist.add(0, verbInterveningSym);
hist.add(0, mappedPrevModSym);
hist.add(1, modEvent.subcat());
hist.add(0, side);
break;
case 2:
// for p(M(t)_i | P, H, verbIntervening, map(M_i-1), subcat, side)
hist.add(0, parent);
hist.add(0, Language.training().removeGapAugmentation(modEvent.head()));
hist.add(0, verbInterveningSym);
hist.add(0, mappedPrevModSym);
hist.add(1, modEvent.subcat());
hist.add(0, side);
break;
case 3:
// for p(M(t)_i | P, subcat)
hist.add(0, parent);
hist.add(1, modEvent.subcat());
break;
}
return hist;
}
private Event getBaseNPHistory(ModifierEvent modEvent, int backOffLevel) {
MutableEvent hist = histories[backOffLevel];
Symbol side = Constants.sideToSym(modEvent.side());
Symbol prevModLabel =
(modEvent.previousMods().get(0) == startSym
? modEvent.head()
: modEvent.previousMods().symbolAt(0));
Word prevModWord =
(modEvent.previousWords().getWord(0).equals(startWord)
? modEvent.headWord()
: modEvent.previousWords().getWord(0));
hist.clear();
switch (backOffLevel) {
case 0:
// for p(t_i | M_i, P, M(w,t)_i-1, side)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
hist.add(prevModLabel);
hist.add(prevModWord.word());
hist.add(prevModWord.tag());
hist.add(side);
break;
case 1:
// for p(t_i | M_i, P, M(t)_i-1, side)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
hist.add(prevModLabel);
hist.add(prevModWord.tag());
hist.add(side);
break;
case 2:
// for p(t_i | M_i, P, M_i-1, side)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
hist.add(prevModLabel);
hist.add(side);
break;
/*
case 3:
// for p(t_i | M_i, P)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
break;
*/
}
return hist;
}
/**
* Representation of the complete back-off structure of the generation model for modifying
* part-of-speech tags (the modifying nonterminals are partially lexicalized with the parts of
* speech of their respective head words, and this model generates the part of speech component of
* these partially-lexicalized nonterminals).
*
* <p><b>It is a horrendous bug that all of these <code>ProbabilityStructure</code> classes do not
* copy various lists from the <code>TrainerEvent</code> objects before removing gap augmentations
* from their elements.</b>
*
* <p>
*/
public class TagModelStructure2 extends ProbabilityStructure {
// data members
private static Symbol startSym = Language.training().startSym();
private static Word startWord = Language.training().startWord();
private Symbol topSym = Language.training().topSym();
public TagModelStructure2() {
super();
}
public int maxEventComponents() {
return 9;
}
public int numLevels() {
return 3;
}
public Event getHistory(TrainerEvent trainerEvent, int backOffLevel) {
ModifierEvent modEvent = (ModifierEvent) trainerEvent;
if (Language.treebank().isBaseNP(modEvent.parent()))
return getBaseNPHistory(modEvent, backOffLevel);
Symbol side = Constants.sideToSym(modEvent.side());
MutableEvent hist = historiesWithSubcats[backOffLevel];
hist.clear();
Symbol verbInterveningSym = Constants.booleanToSym(modEvent.verbIntervening());
Symbol mappedPrevModSym = NTMapper.map(modEvent.previousMods().symbolAt(0));
Symbol parent = Language.training().removeArgAugmentation(modEvent.parent());
switch (backOffLevel) {
case 0:
// for p(t_i | M_i, P, H, w, t, verbIntervening, map(M_i-1),
// subcat, side)
hist.add(0, modEvent.modifier());
hist.add(0, parent);
hist.add(0, Language.training().removeGapAugmentation(modEvent.head()));
hist.add(0, modEvent.headWord().word());
hist.add(0, modEvent.headWord().tag());
hist.add(0, verbInterveningSym);
hist.add(0, mappedPrevModSym);
hist.add(1, modEvent.subcat());
hist.add(0, side);
break;
case 1:
// for p(t_i | M_i, P, H, t, verbIntervening, map(M_i-1), subcat, side)
hist.add(0, modEvent.modifier());
hist.add(0, parent);
hist.add(0, Language.training().removeGapAugmentation(modEvent.head()));
hist.add(0, modEvent.headWord().tag());
hist.add(0, verbInterveningSym);
hist.add(0, mappedPrevModSym);
hist.add(1, modEvent.subcat());
hist.add(0, side);
break;
case 2:
// for p(t_i | M_i, P, H, verbIntervening, map(M_i-1), subcat, side)
hist.add(0, modEvent.modifier());
hist.add(0, parent);
hist.add(0, Language.training().removeGapAugmentation(modEvent.head()));
hist.add(0, verbInterveningSym);
hist.add(0, mappedPrevModSym);
hist.add(1, modEvent.subcat());
hist.add(0, side);
break;
/*
case 3:
// for p(t_i | M_i, P, subcat)
hist.add(0, modEvent.modifier());
hist.add(0, parent);
hist.add(1, modEvent.subcat());
break;
*/
}
return hist;
}
private Event getBaseNPHistory(ModifierEvent modEvent, int backOffLevel) {
MutableEvent hist = histories[backOffLevel];
Symbol side = Constants.sideToSym(modEvent.side());
Symbol prevModLabel =
(modEvent.previousMods().get(0) == startSym
? modEvent.head()
: modEvent.previousMods().symbolAt(0));
Word prevModWord =
(modEvent.previousWords().getWord(0).equals(startWord)
? modEvent.headWord()
: modEvent.previousWords().getWord(0));
hist.clear();
switch (backOffLevel) {
case 0:
// for p(t_i | M_i, P, M(w,t)_i-1, side)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
hist.add(prevModLabel);
hist.add(prevModWord.word());
hist.add(prevModWord.tag());
hist.add(side);
break;
case 1:
// for p(t_i | M_i, P, M(t)_i-1, side)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
hist.add(prevModLabel);
hist.add(prevModWord.tag());
hist.add(side);
break;
case 2:
// for p(t_i | M_i, P, M_i-1, side)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
hist.add(prevModLabel);
hist.add(side);
break;
/*
case 3:
// for p(t_i | M_i, P)
hist.add(modEvent.modifier());
hist.add(Language.training().removeGapAugmentation(modEvent.parent()));
break;
*/
}
return hist;
}
public Event getFuture(TrainerEvent trainerEvent, int backOffLevel) {
ModifierEvent modEvent = (ModifierEvent) trainerEvent;
MutableEvent future = futures[backOffLevel];
future.clear();
future.add(modEvent.modHeadWord().tag());
return future;
}
public boolean doCleanup() {
return true;
}
/**
* In order to gather statistics for words that appear as the head of the entire sentence when
* estimating p(w | t), the trainer "fakes" a modifier event, as though the root node of the
* observed tree was seen to modify the magical +TOP+ node. We will never use the derived counts
* whose history contexts contain +TOP+. This method allows for the removal of these "unnecessary"
* counts, which will never be used when decoding.
*/
public boolean removeHistory(int backOffLevel, Event history) {
return history.get(0, 1) == topSym;
}
public ProbabilityStructure copy() {
return new TagModelStructure2();
}
}