/**
* Return the probability (as a real number between 0 and 1) of stopping rather than generating
* another argument at this position.
*
* @param dependency The dependency used as the basis for stopping on. Tags are assumed to be in
* the TagProjection space.
* @return The probability of generating this stop probability
*/
protected double getStopProb(IntDependency dependency) {
short binDistance = distanceBin(dependency.distance);
IntTaggedWord unknownHead = new IntTaggedWord(-1, dependency.head.tag);
IntTaggedWord anyHead = new IntTaggedWord(ANY_WORD_INT, dependency.head.tag);
IntDependency temp =
new IntDependency(dependency.head, stopTW, dependency.leftHeaded, binDistance);
double c_stop_hTWds = stopCounter.getCount(temp);
temp = new IntDependency(unknownHead, stopTW, dependency.leftHeaded, binDistance);
double c_stop_hTds = stopCounter.getCount(temp);
temp = new IntDependency(dependency.head, wildTW, dependency.leftHeaded, binDistance);
double c_hTWds = stopCounter.getCount(temp);
temp = new IntDependency(anyHead, wildTW, dependency.leftHeaded, binDistance);
double c_hTds = stopCounter.getCount(temp);
double p_stop_hTds = (c_hTds > 0.0 ? c_stop_hTds / c_hTds : 1.0);
double pb_stop_hTWds = (c_stop_hTWds + smooth_stop * p_stop_hTds) / (c_hTWds + smooth_stop);
if (verbose) {
System.out.println(
" c_stop_hTWds: "
+ c_stop_hTWds
+ "; c_hTWds: "
+ c_hTWds
+ "; c_stop_hTds: "
+ c_stop_hTds
+ "; c_hTds: "
+ c_hTds);
System.out.println(" Generate STOP prob: " + pb_stop_hTWds);
}
return pb_stop_hTWds;
}
private void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException {
stream.defaultReadObject();
// System.err.println("Before decompression:");
// System.err.println("arg size: " + argCounter.size() + " total: " +
// argCounter.totalCount());
// System.err.println("stop size: " + stopCounter.size() + " total: " +
// stopCounter.totalCount());
ClassicCounter<IntDependency> compressedArgC = argCounter;
argCounter = new ClassicCounter<IntDependency>();
ClassicCounter<IntDependency> compressedStopC = stopCounter;
stopCounter = new ClassicCounter<IntDependency>();
for (IntDependency d : compressedArgC.keySet()) {
double count = compressedArgC.getCount(d);
expandArg(d, d.distance, count);
}
for (IntDependency d : compressedStopC.keySet()) {
double count = compressedStopC.getCount(d);
expandStop(d, d.distance, count, false);
}
// System.err.println("After decompression:");
// System.err.println("arg size: " + argCounter.size() + " total: " +
// argCounter.totalCount());
// System.err.println("stop size: " + stopCounter.size() + " total: " +
// stopCounter.totalCount());
expandDependencyMap = null;
}
/** Writes out data from this Object to the Writer w. */
@Override
public void writeData(PrintWriter out) throws IOException {
// all lines have one rule per line
for (IntDependency dependency : argCounter.keySet()) {
if (dependency.head != wildTW
&& dependency.arg != wildTW
&& dependency.head.word != -1
&& dependency.arg.word != -1) {
double count = argCounter.getCount(dependency);
out.println(dependency.toString(wordIndex, tagIndex) + " " + count);
}
}
out.println("BEGIN_STOP");
for (IntDependency dependency : stopCounter.keySet()) {
if (dependency.head.word != -1) {
double count = stopCounter.getCount(dependency);
out.println(dependency.toString(wordIndex, tagIndex) + " " + count);
}
}
out.flush();
}
/**
* Evaluates how many words (= terminals) in a collection of trees are covered by the lexicon.
* First arg is the collection of trees; second through fourth args get the results. Currently
* unused; this probably only works if train and test at same time so tags and words variables are
* initialized.
*/
public double evaluateCoverage(
Collection<Tree> trees,
Set<String> missingWords,
Set<String> missingTags,
Set<IntTaggedWord> missingTW) {
List<IntTaggedWord> iTW1 = new ArrayList<IntTaggedWord>();
for (Tree t : trees) {
iTW1.addAll(treeToEvents(t));
}
int total = 0;
int unseen = 0;
for (IntTaggedWord itw : iTW1) {
total++;
if (!words.contains(new IntTaggedWord(itw.word(), nullTag))) {
missingWords.add(wordIndex.get(itw.word()));
}
if (!tags.contains(new IntTaggedWord(nullWord, itw.tag()))) {
missingTags.add(tagIndex.get(itw.tag()));
}
// if (!rules.contains(itw)) {
if (seenCounter.getCount(itw) == 0.0) {
unseen++;
missingTW.add(itw);
}
}
return (double) unseen / total;
}
private static <T> void display(ClassicCounter<T> c, PrintWriter pw) {
List<T> cats = new ArrayList<>(c.keySet());
Collections.sort(cats, Counters.toComparatorDescending(c));
for (T ob : cats) {
pw.println(ob + " " + c.getCount(ob));
}
}
/**
* This records how likely it is for a word with one tag to also have another tag. This won't work
* after serialization/deserialization, but that is how it is currently called....
*/
void buildPT_T() {
int numTags = tagIndex.size();
m_TT = new double[numTags][numTags];
m_T = new double[numTags];
double[] tmp = new double[numTags];
for (IntTaggedWord word : words) {
double tot = 0.0;
for (int t = 0; t < numTags; t++) {
IntTaggedWord iTW = new IntTaggedWord(word.word, t);
tmp[t] = seenCounter.getCount(iTW);
tot += tmp[t];
}
if (tot < 10) {
continue;
}
for (int t = 0; t < numTags; t++) {
for (int t2 = 0; t2 < numTags; t2++) {
if (tmp[t2] > 0.0) {
double c = tmp[t] / tot;
m_T[t] += c;
m_TT[t2][t] += c;
}
}
}
}
}
private static <T> void display(ClassicCounter<T> c, int num, PrintWriter pw) {
List<T> rules = new ArrayList<>(c.keySet());
Collections.sort(rules, Counters.toComparatorDescending(c));
int rSize = rules.size();
if (num > rSize) {
num = rSize;
}
for (int i = 0; i < num; i++) {
pw.println(rules.get(i) + " " + c.getCount(rules.get(i)));
}
}
public double countHistory(IntDependency dependency) {
IntDependency temp =
new IntDependency(
dependency.head.word,
tagBin(dependency.head.tag),
wildTW.word,
wildTW.tag,
dependency.leftHeaded,
valenceBin(dependency.distance));
return argCounter.getCount(temp);
}
/** @param <T> */
public static <T> List<FeatureValue<T>> combine(Collection<FeatureValue<T>> featureValues) {
ClassicCounter<T> counter = new ClassicCounter<T>();
for (FeatureValue<T> fv : featureValues) {
counter.incrementCount(fv.name, fv.value);
}
Set<T> keys = new TreeSet<T>(counter.keySet());
List<FeatureValue<T>> featureList = new ArrayList<FeatureValue<T>>(keys.size());
for (T key : keys) {
featureList.add(new FeatureValue<T>(key, counter.getCount(key)));
}
return featureList;
}
/**
* Generate the possible taggings for a word at a sentence position. This may either be based on a
* strict lexicon or an expanded generous set of possible taggings.
*
* <p><i>Implementation note:</i> Expanded sets of possible taggings are calculated dynamically at
* runtime, so as to reduce the memory used by the lexicon (a space/time tradeoff).
*
* @param word The word (as an int)
* @param loc Its index in the sentence (usually only relevant for unknown words)
* @return A list of possible taggings
*/
public Iterator<IntTaggedWord> ruleIteratorByWord(int word, int loc, String featureSpec) {
// if (rulesWithWord == null) { // tested in isKnown already
// initRulesWithWord();
// }
List<IntTaggedWord> wordTaggings;
if (isKnown(word)) {
if (!flexiTag) {
// Strict lexical tagging for seen items
wordTaggings = rulesWithWord[word];
} else {
/* Allow all tags with same basicCategory */
/* Allow all scored taggings, unless very common */
IntTaggedWord iW = new IntTaggedWord(word, nullTag);
if (seenCounter.getCount(iW) > smoothInUnknownsThreshold) {
return rulesWithWord[word].iterator();
} else {
// give it flexible tagging not just lexicon
wordTaggings = new ArrayList<IntTaggedWord>(40);
for (IntTaggedWord iTW2 : tags) {
IntTaggedWord iTW = new IntTaggedWord(word, iTW2.tag);
if (score(iTW, loc, wordIndex.get(word)) > Float.NEGATIVE_INFINITY) {
wordTaggings.add(iTW);
}
}
}
}
} else {
// we copy list so we can insert correct word in each item
wordTaggings = new ArrayList<IntTaggedWord>(40);
for (IntTaggedWord iTW : rulesWithWord[wordIndex.indexOf(UNKNOWN_WORD)]) {
wordTaggings.add(new IntTaggedWord(word, iTW.tag));
}
}
if (DEBUG_LEXICON) {
EncodingPrintWriter.err.println(
"Lexicon: "
+ wordIndex.get(word)
+ " ("
+ (isKnown(word) ? "known" : "unknown")
+ ", loc="
+ loc
+ ", n="
+ (isKnown(word) ? word : wordIndex.indexOf(UNKNOWN_WORD))
+ ") "
+ (flexiTag ? "flexi" : "lexicon")
+ " taggings: "
+ wordTaggings,
"UTF-8");
}
return wordTaggings.iterator();
}
private void writeObject(ObjectOutputStream stream) throws IOException {
// System.err.println("\nBefore compression:");
// System.err.println("arg size: " + argCounter.size() + " total: " +
// argCounter.totalCount());
// System.err.println("stop size: " + stopCounter.size() + " total: " +
// stopCounter.totalCount());
ClassicCounter<IntDependency> fullArgCounter = argCounter;
argCounter = new ClassicCounter<IntDependency>();
for (IntDependency dependency : fullArgCounter.keySet()) {
if (dependency.head != wildTW
&& dependency.arg != wildTW
&& dependency.head.word != -1
&& dependency.arg.word != -1) {
argCounter.incrementCount(dependency, fullArgCounter.getCount(dependency));
}
}
ClassicCounter<IntDependency> fullStopCounter = stopCounter;
stopCounter = new ClassicCounter<IntDependency>();
for (IntDependency dependency : fullStopCounter.keySet()) {
if (dependency.head.word != -1) {
stopCounter.incrementCount(dependency, fullStopCounter.getCount(dependency));
}
}
// System.err.println("After compression:");
// System.err.println("arg size: " + argCounter.size() + " total: " +
// argCounter.totalCount());
// System.err.println("stop size: " + stopCounter.size() + " total: " +
// stopCounter.totalCount());
stream.defaultWriteObject();
argCounter = fullArgCounter;
stopCounter = fullStopCounter;
}
/**
* Writes out data from this Object to the Writer w. Rules are separated by newline, and rule
* elements are delimited by \t.
*/
public void writeData(Writer w) throws IOException {
PrintWriter out = new PrintWriter(w);
for (IntTaggedWord itw : seenCounter.keySet()) {
out.println(itw.toLexicalEntry(wordIndex, tagIndex) + " SEEN " + seenCounter.getCount(itw));
}
for (IntTaggedWord itw : getUnknownWordModel().unSeenCounter().keySet()) {
out.println(
itw.toLexicalEntry(wordIndex, tagIndex)
+ " UNSEEN "
+ getUnknownWordModel().unSeenCounter().getCount(itw));
}
for (int i = 0; i < smooth.length; i++) {
out.println("smooth[" + i + "] = " + smooth[i]);
}
out.flush();
}
@Override
public DependencyGrammar formResult() {
wordIndex.indexOf(Lexicon.UNKNOWN_WORD, true);
MLEDependencyGrammar dg =
new MLEDependencyGrammar(
tlpParams,
directional,
useDistance,
useCoarseDistance,
basicCategoryTagsInDependencyGrammar,
op,
wordIndex,
tagIndex);
for (IntDependency dependency : dependencyCounter.keySet()) {
dg.addRule(dependency, dependencyCounter.getCount(dependency));
}
return dg;
}
/** Trains this UWM on the Collection of trees. */
public void train(TaggedWord tw, int loc, double weight) {
IntTaggedWord iTW = new IntTaggedWord(tw.word(), tw.tag(), wordIndex, tagIndex);
IntTaggedWord iT = new IntTaggedWord(nullWord, iTW.tag);
IntTaggedWord iW = new IntTaggedWord(iTW.word, nullTag);
seenCounter.incrementCount(iW, weight);
IntTaggedWord i = NULL_ITW;
if (treesRead > indexToStartUnkCounting) {
// start doing this once some way through trees;
// treesRead is 1 based counting
if (seenCounter.getCount(iW) < 1.5) {
// it's an entirely unknown word
int s = model.getSignatureIndex(iTW.word, loc, wordIndex.get(iTW.word));
if (DOCUMENT_UNKNOWNS) {
String wStr = wordIndex.get(iTW.word);
String tStr = tagIndex.get(iTW.tag);
String sStr = wordIndex.get(s);
EncodingPrintWriter.err.println(
"Unknown word/tag/sig:\t" + wStr + '\t' + tStr + '\t' + sStr, "UTF-8");
}
IntTaggedWord iTS = new IntTaggedWord(s, iTW.tag);
IntTaggedWord iS = new IntTaggedWord(s, nullTag);
unSeenCounter.incrementCount(iTS, weight);
unSeenCounter.incrementCount(iT, weight);
unSeenCounter.incrementCount(iS, weight);
unSeenCounter.incrementCount(i, weight);
// rules.add(iTS);
// sigs.add(iS);
} // else {
// if (seenCounter.getCount(iTW) < 2) {
// it's a new tag for a known word
// do nothing for now
// }
// }
}
}
/**
* Checks whether a word is in the lexicon. This version works even while compiling lexicon with
* current counters (rather than using the compiled rulesWithWord array).
*
* <p>TODO: The previous version would insert rules into the wordNumberer. Is that the desired
* behavior? Why not test in some way that doesn't affect the index? For example, start by testing
* wordIndex.contains(word).
*
* @param word The word as a String
* @return Whether the word is in the lexicon
*/
public boolean isKnown(String word) {
if (!wordIndex.contains(word)) return false;
IntTaggedWord iW = new IntTaggedWord(wordIndex.indexOf(word), nullTag);
return seenCounter.getCount(iW) > 0.0;
}
/**
* Get the score of this word with this tag (as an IntTaggedWord) at this location. (Presumably an
* estimate of P(word | tag).)
*
* <p><i>Implementation documentation:</i> Seen: c_W = count(W) c_TW = count(T,W) c_T = count(T)
* c_Tunseen = count(T) among new words in 2nd half total = count(seen words) totalUnseen =
* count("unseen" words) p_T_U = Pmle(T|"unseen") pb_T_W = P(T|W). If (c_W >
* smoothInUnknownsThreshold) = c_TW/c_W Else (if not smart mutation) pb_T_W = bayes prior
* smooth[1] with p_T_U p_T= Pmle(T) p_W = Pmle(W) pb_W_T = log(pb_T_W * p_W / p_T) [Bayes rule]
* Note that this doesn't really properly reserve mass to unknowns.
*
* <p>Unseen: c_TS = count(T,Sig|Unseen) c_S = count(Sig) c_T = count(T|Unseen) c_U = totalUnseen
* above p_T_U = Pmle(T|Unseen) pb_T_S = Bayes smooth of Pmle(T|S) with P(T|Unseen) [smooth[0]]
* pb_W_T = log(P(W|T)) inverted
*
* @param iTW An IntTaggedWord pairing a word and POS tag
* @param loc The position in the sentence. <i>In the default implementation this is used only for
* unknown words to change their probability distribution when sentence initial</i>
* @return A float score, usually, log P(word|tag)
*/
public float score(IntTaggedWord iTW, int loc, String word) {
// both actual
double c_TW = seenCounter.getCount(iTW);
// double x_TW = xferCounter.getCount(iTW);
IntTaggedWord temp = new IntTaggedWord(iTW.word, nullTag);
// word counts
double c_W = seenCounter.getCount(temp);
// double x_W = xferCounter.getCount(temp);
// totals
double total = seenCounter.getCount(NULL_ITW);
double totalUnseen = uwModel.unSeenCounter().getCount(NULL_ITW);
temp = new IntTaggedWord(nullWord, iTW.tag);
// tag counts
double c_T = seenCounter.getCount(temp);
double c_Tunseen = uwModel.unSeenCounter().getCount(temp);
double pb_W_T; // always set below
if (DEBUG_LEXICON) {
// dump info about last word
if (iTW.word != debugLastWord) {
if (debugLastWord >= 0 && debugPrefix != null) {
// the 2nd conjunct in test above handles older serialized files
EncodingPrintWriter.err.println(debugPrefix + debugProbs + debugNoProbs, "UTF-8");
}
}
}
boolean seen = (c_W > 0.0);
if (seen) {
// known word model for P(T|W)
if (DEBUG_LEXICON_SCORE) {
System.err.println(
"Lexicon.score "
+ wordIndex.get(iTW.word)
+ "/"
+ tagIndex.get(iTW.tag)
+ " as known word.");
}
// c_TW = Math.sqrt(c_TW); [cdm: funny math scaling? dunno who played with this]
// c_TW += 0.5;
double p_T_U;
if (useSignatureForKnownSmoothing) { // only works for English currently
p_T_U = getUnknownWordModel().scoreProbTagGivenWordSignature(iTW, loc, smooth[0], word);
if (DEBUG_LEXICON_SCORE)
System.err.println(
"With useSignatureForKnownSmoothing, P(T|U) is "
+ p_T_U
+ " rather than "
+ (c_Tunseen / totalUnseen));
} else {
p_T_U = c_Tunseen / totalUnseen;
}
double pb_T_W; // always set below
if (DEBUG_LEXICON_SCORE) {
System.err.println(
"c_W is "
+ c_W
+ " mle = "
+ (c_TW / c_W)
+ " smoothInUnknownsThresh is "
+ smoothInUnknownsThreshold
+ " base p_T_U is "
+ c_Tunseen
+ "/"
+ totalUnseen
+ " = "
+ p_T_U);
}
if (c_W > smoothInUnknownsThreshold && c_TW > 0.0 && c_W > 0.0) {
// we've seen the word enough times to have confidence in its tagging
pb_T_W = c_TW / c_W;
} else {
// we haven't seen the word enough times to have confidence in its
// tagging
if (smartMutation) {
int numTags = tagIndex.size();
if (m_TT == null || numTags != m_T.length) {
buildPT_T();
}
p_T_U *= 0.1;
// System.out.println("Checking "+iTW);
for (int t = 0; t < numTags; t++) {
IntTaggedWord iTW2 = new IntTaggedWord(iTW.word, t);
double p_T_W2 = seenCounter.getCount(iTW2) / c_W;
if (p_T_W2 > 0) {
// System.out.println(" Observation of "+tagIndex.get(t)+"
// ("+seenCounter.getCount(iTW2)+") mutated to
// "+tagIndex.get(iTW.tag)+" at rate
// "+(m_TT[tag][t]/m_T[t]));
p_T_U += p_T_W2 * m_TT[iTW.tag][t] / m_T[t] * 0.9;
}
}
}
if (DEBUG_LEXICON_SCORE) {
System.err.println("c_TW = " + c_TW + " c_W = " + c_W + " p_T_U = " + p_T_U);
}
// double pb_T_W = (c_TW+smooth[1]*x_TW)/(c_W+smooth[1]*x_W);
pb_T_W = (c_TW + smooth[1] * p_T_U) / (c_W + smooth[1]);
}
double p_T = (c_T / total);
double p_W = (c_W / total);
pb_W_T = Math.log(pb_T_W * p_W / p_T);
if (DEBUG_LEXICON) {
if (iTW.word != debugLastWord) {
debugLastWord = iTW.word;
debugLoc = loc;
debugProbs = new StringBuilder();
debugNoProbs = new StringBuilder("impossible: ");
debugPrefix = "Lexicon: " + wordIndex.get(debugLastWord) + " (known): ";
}
if (pb_W_T > Double.NEGATIVE_INFINITY) {
NumberFormat nf = NumberFormat.getNumberInstance();
nf.setMaximumFractionDigits(3);
debugProbs.append(
tagIndex.get(iTW.tag)
+ ": cTW="
+ c_TW
+ " c_T="
+ c_T
+ " pb_T_W="
+ nf.format(pb_T_W)
+ " log pb_W_T="
+ nf.format(pb_W_T)
+ ", ");
// debugProbs.append("\n" + "smartMutation=" + smartMutation + "
// smoothInUnknownsThreshold=" + smoothInUnknownsThreshold + "
// smooth0=" + smooth[0] + "smooth1=" + smooth[1] + " p_T_U=" + p_T_U
// + " c_W=" + c_W);
} else {
debugNoProbs.append(tagIndex.get(iTW.tag)).append(' ');
}
} // end if (DEBUG_LEXICON)
} else { // when unseen
if (loc >= 0) {
pb_W_T = getUnknownWordModel().score(iTW, loc, c_T, total, smooth[0], word);
} else {
// For negative we now do a weighted average for the dependency grammar :-)
double pb_W0_T = getUnknownWordModel().score(iTW, 0, c_T, total, smooth[0], word);
double pb_W1_T = getUnknownWordModel().score(iTW, 1, c_T, total, smooth[0], word);
pb_W_T = Math.log((Math.exp(pb_W0_T) + 2 * Math.exp(pb_W1_T)) / 3);
}
}
// Categorical cutoff if score is too low
if (pb_W_T > -100.0) {
return (float) pb_W_T;
}
return Float.NEGATIVE_INFINITY;
} // end score()
/**
* Calculate the probability of a dependency as a real probability between 0 and 1 inclusive.
*
* @param dependency The dependency for which the probability is to be calculated. The tags in
* this dependency are in the reduced TagProjection space.
* @return The probability of the dependency
*/
protected double probTB(IntDependency dependency) {
if (verbose) {
// System.out.println("tagIndex: " + tagIndex);
System.err.println("Generating " + dependency);
}
boolean leftHeaded = dependency.leftHeaded && directional;
int hW = dependency.head.word;
int aW = dependency.arg.word;
short hT = dependency.head.tag;
short aT = dependency.arg.tag;
IntTaggedWord aTW = dependency.arg;
IntTaggedWord hTW = dependency.head;
boolean isRoot = rootTW(dependency.head);
double pb_stop_hTWds;
if (isRoot) {
pb_stop_hTWds = 0.0;
} else {
pb_stop_hTWds = getStopProb(dependency);
}
if (dependency.arg.word == STOP_WORD_INT) {
// did we generate stop?
return pb_stop_hTWds;
}
double pb_go_hTWds = 1.0 - pb_stop_hTWds;
// generate the argument
short binDistance = valenceBin(dependency.distance);
// KEY:
// c_ count of (read as joint count of first and second)
// p_ MLE prob of (or MAP if useSmoothTagProjection)
// pb_ MAP prob of (read as prob of first given second thing)
// a arg
// h head
// T tag
// PT projected tag
// W word
// d direction
// ds distance (implicit: there when direction is mentioned!)
IntTaggedWord anyHead = new IntTaggedWord(ANY_WORD_INT, dependency.head.tag);
IntTaggedWord anyArg = new IntTaggedWord(ANY_WORD_INT, dependency.arg.tag);
IntTaggedWord anyTagArg = new IntTaggedWord(dependency.arg.word, ANY_TAG_INT);
IntDependency temp =
new IntDependency(dependency.head, dependency.arg, leftHeaded, binDistance);
double c_aTW_hTWd = argCounter.getCount(temp);
temp = new IntDependency(dependency.head, anyArg, leftHeaded, binDistance);
double c_aT_hTWd = argCounter.getCount(temp);
temp = new IntDependency(dependency.head, wildTW, leftHeaded, binDistance);
double c_hTWd = argCounter.getCount(temp);
temp = new IntDependency(anyHead, dependency.arg, leftHeaded, binDistance);
double c_aTW_hTd = argCounter.getCount(temp);
temp = new IntDependency(anyHead, anyArg, leftHeaded, binDistance);
double c_aT_hTd = argCounter.getCount(temp);
temp = new IntDependency(anyHead, wildTW, leftHeaded, binDistance);
double c_hTd = argCounter.getCount(temp);
// for smooth tag projection
short aPT = Short.MIN_VALUE;
double c_aPTW_hPTd = Double.NaN;
double c_aPT_hPTd = Double.NaN;
double c_hPTd = Double.NaN;
double c_aPTW_aPT = Double.NaN;
double c_aPT = Double.NaN;
if (useSmoothTagProjection) {
aPT = tagProject(dependency.arg.tag);
short hPT = tagProject(dependency.head.tag);
IntTaggedWord projectedArg = new IntTaggedWord(dependency.arg.word, aPT);
IntTaggedWord projectedAnyHead = new IntTaggedWord(ANY_WORD_INT, hPT);
IntTaggedWord projectedAnyArg = new IntTaggedWord(ANY_WORD_INT, aPT);
temp = new IntDependency(projectedAnyHead, projectedArg, leftHeaded, binDistance);
c_aPTW_hPTd = argCounter.getCount(temp);
temp = new IntDependency(projectedAnyHead, projectedAnyArg, leftHeaded, binDistance);
c_aPT_hPTd = argCounter.getCount(temp);
temp = new IntDependency(projectedAnyHead, wildTW, leftHeaded, binDistance);
c_hPTd = argCounter.getCount(temp);
temp = new IntDependency(wildTW, projectedArg, false, ANY_DISTANCE_INT);
c_aPTW_aPT = argCounter.getCount(temp);
temp = new IntDependency(wildTW, projectedAnyArg, false, ANY_DISTANCE_INT);
c_aPT = argCounter.getCount(temp);
}
// wild head is always directionless and no use distance
temp = new IntDependency(wildTW, dependency.arg, false, ANY_DISTANCE_INT);
double c_aTW = argCounter.getCount(temp);
temp = new IntDependency(wildTW, anyArg, false, ANY_DISTANCE_INT);
double c_aT = argCounter.getCount(temp);
temp = new IntDependency(wildTW, anyTagArg, false, ANY_DISTANCE_INT);
double c_aW = argCounter.getCount(temp);
// do the Bayesian magic
// MLE probs
double p_aTW_hTd;
double p_aT_hTd;
double p_aTW_aT;
double p_aW;
double p_aPTW_aPT;
double p_aPTW_hPTd;
double p_aPT_hPTd;
// backoffs either mle or themselves bayesian smoothed depending on useSmoothTagProjection
if (useSmoothTagProjection) {
if (useUnigramWordSmoothing) {
p_aW = c_aW > 0.0 ? (c_aW / numWordTokens) : 1.0; // NEED this 1.0 for unknown words!!!
p_aPTW_aPT = (c_aPTW_aPT + smooth_aPTW_aPT * p_aW) / (c_aPT + smooth_aPTW_aPT);
} else {
p_aPTW_aPT =
c_aPTW_aPT > 0.0 ? (c_aPTW_aPT / c_aPT) : 1.0; // NEED this 1.0 for unknown words!!!
}
p_aTW_aT = (c_aTW + smooth_aTW_aT * p_aPTW_aPT) / (c_aT + smooth_aTW_aT);
p_aPTW_hPTd = c_hPTd > 0.0 ? (c_aPTW_hPTd / c_hPTd) : 0.0;
p_aTW_hTd = (c_aTW_hTd + smooth_aTW_hTd * p_aPTW_hPTd) / (c_hTd + smooth_aTW_hTd);
p_aPT_hPTd = c_hPTd > 0.0 ? (c_aPT_hPTd / c_hPTd) : 0.0;
p_aT_hTd = (c_aT_hTd + smooth_aT_hTd * p_aPT_hPTd) / (c_hTd + smooth_aT_hTd);
} else {
// here word generation isn't smoothed - can't get previously unseen word with tag. Ugh.
if (op.testOptions.useLexiconToScoreDependencyPwGt) {
// We don't know the position. Now -1 means average over 0 and 1.
p_aTW_aT =
dependency.leftHeaded
? Math.exp(lex.score(dependency.arg, 1, wordIndex.get(dependency.arg.word)))
: Math.exp(lex.score(dependency.arg, -1, wordIndex.get(dependency.arg.word)));
// double oldScore = c_aTW > 0.0 ? (c_aTW / c_aT) : 1.0;
// if (oldScore == 1.0) {
// System.err.println("#### arg=" + dependency.arg + " score=" + p_aTW_aT +
// " oldScore=" + oldScore + " c_aTW=" + c_aTW + " c_aW=" + c_aW);
// }
} else {
p_aTW_aT = c_aTW > 0.0 ? (c_aTW / c_aT) : 1.0;
}
p_aTW_hTd = c_hTd > 0.0 ? (c_aTW_hTd / c_hTd) : 0.0;
p_aT_hTd = c_hTd > 0.0 ? (c_aT_hTd / c_hTd) : 0.0;
}
double pb_aTW_hTWd = (c_aTW_hTWd + smooth_aTW_hTWd * p_aTW_hTd) / (c_hTWd + smooth_aTW_hTWd);
double pb_aT_hTWd = (c_aT_hTWd + smooth_aT_hTWd * p_aT_hTd) / (c_hTWd + smooth_aT_hTWd);
double score = (interp * pb_aTW_hTWd + (1.0 - interp) * p_aTW_aT * pb_aT_hTWd) * pb_go_hTWds;
if (verbose) {
NumberFormat nf = NumberFormat.getNumberInstance();
nf.setMaximumFractionDigits(2);
if (useSmoothTagProjection) {
if (useUnigramWordSmoothing) {
System.err.println(
" c_aW=" + c_aW + ", numWordTokens=" + numWordTokens + ", p(aW)=" + nf.format(p_aW));
}
System.err.println(
" c_aPTW_aPT="
+ c_aPTW_aPT
+ ", c_aPT="
+ c_aPT
+ ", smooth_aPTW_aPT="
+ smooth_aPTW_aPT
+ ", p(aPTW|aPT)="
+ nf.format(p_aPTW_aPT));
}
System.err.println(
" c_aTW="
+ c_aTW
+ ", c_aT="
+ c_aT
+ ", smooth_aTW_aT="
+ smooth_aTW_aT
+ ", ## p(aTW|aT)="
+ nf.format(p_aTW_aT));
if (useSmoothTagProjection) {
System.err.println(
" c_aPTW_hPTd="
+ c_aPTW_hPTd
+ ", c_hPTd="
+ c_hPTd
+ ", p(aPTW|hPTd)="
+ nf.format(p_aPTW_hPTd));
}
System.err.println(
" c_aTW_hTd="
+ c_aTW_hTd
+ ", c_hTd="
+ c_hTd
+ ", smooth_aTW_hTd="
+ smooth_aTW_hTd
+ ", p(aTW|hTd)="
+ nf.format(p_aTW_hTd));
if (useSmoothTagProjection) {
System.err.println(
" c_aPT_hPTd="
+ c_aPT_hPTd
+ ", c_hPTd="
+ c_hPTd
+ ", p(aPT|hPTd)="
+ nf.format(p_aPT_hPTd));
}
System.err.println(
" c_aT_hTd="
+ c_aT_hTd
+ ", c_hTd="
+ c_hTd
+ ", smooth_aT_hTd="
+ smooth_aT_hTd
+ ", p(aT|hTd)="
+ nf.format(p_aT_hTd));
System.err.println(
" c_aTW_hTWd="
+ c_aTW_hTWd
+ ", c_hTWd="
+ c_hTWd
+ ", smooth_aTW_hTWd="
+ smooth_aTW_hTWd
+ ", ## p(aTW|hTWd)="
+ nf.format(pb_aTW_hTWd));
System.err.println(
" c_aT_hTWd="
+ c_aT_hTWd
+ ", c_hTWd="
+ c_hTWd
+ ", smooth_aT_hTWd="
+ smooth_aT_hTWd
+ ", ## p(aT|hTWd)="
+ nf.format(pb_aT_hTWd));
System.err.println(
" interp="
+ interp
+ ", prescore="
+ nf.format(interp * pb_aTW_hTWd + (1.0 - interp) * p_aTW_aT * pb_aT_hTWd)
+ ", P(go|hTWds)="
+ nf.format(pb_go_hTWds)
+ ", score="
+ nf.format(score));
}
if (op.testOptions.prunePunc && pruneTW(aTW)) {
return 1.0;
}
if (Double.isNaN(score)) {
score = 0.0;
}
// if (op.testOptions.rightBonus && ! dependency.leftHeaded)
// score -= 0.2;
if (score < MIN_PROBABILITY) {
score = 0.0;
}
return score;
}
