/**
* Reads parse trees either from standard input or a specified file, converting them to sentences
* and printing those sentences on standard output.
*
* <pre>
* usage: [-v|-help|-usage] [-tags] [filename]
* -v|-help|-usage: prints out this message
* -tags: indicates to spit out one S-expression per word, of the form
* (word (tag))
* filename is the file to be processed (standard input is assumed if
* this argument is "-" or is not present)
* </pre>
*/
public static void main(String[] args) {
InputStream inStream = System.in;
boolean tags = false;
String inFile = null;
for (int i = 0; i < args.length; i++) {
if (args[i].equals("-help") || args[i].equals("-usage") || args[i].equals("-v")) {
usage();
return;
} else if (args[i].equals("-tags")) tags = true;
else if (!args[i].equals("-")) inFile = args[i];
}
if (inFile != null) {
try {
inStream = new FileInputStream(inFile);
} catch (FileNotFoundException fnfe) {
System.err.println(fnfe);
System.exit(-1);
}
}
try {
SexpTokenizer tok = new SexpTokenizer(inStream, Language.encoding(), bufSize);
OutputStream os = System.out;
Writer writer = new BufferedWriter(new OutputStreamWriter(os, Language.encoding()));
PrintWriter pw = new PrintWriter(writer);
Sexp curr = null;
while ((curr = Sexp.read(tok)) != null)
pw.println(tags ? Util.collectTaggedWords(curr) : Util.collectLeaves(curr));
pw.flush();
pw.close();
} catch (Exception e) {
System.out.println(e);
}
}
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();
}
}
