/**
* 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;
}
/**
* 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();
}
/** Adds the tagging with count to the data structures in this Lexicon. */
protected void addTagging(boolean seen, IntTaggedWord itw, double count) {
if (seen) {
seenCounter.incrementCount(itw, count);
if (itw.tag() == nullTag) {
words.add(itw);
} else if (itw.word() == nullWord) {
tags.add(itw);
} else {
// rules.add(itw);
}
} else {
uwModel.addTagging(seen, itw, count);
// if (itw.tag() == nullTag) {
// sigs.add(itw);
// }
}
}
protected void initRulesWithWord() {
if (testOptions.verbose || DEBUG_LEXICON) {
System.err.print("\nInitializing lexicon scores ... ");
}
// int numWords = words.size()+sigs.size()+1;
int unkWord = wordIndex.indexOf(UNKNOWN_WORD, true);
int numWords = wordIndex.size();
rulesWithWord = new List[numWords];
for (int w = 0; w < numWords; w++) {
rulesWithWord[w] = new ArrayList<IntTaggedWord>(1); // most have 1 or 2
// items in them
}
// for (Iterator ruleI = rules.iterator(); ruleI.hasNext();) {
tags = new HashSet<IntTaggedWord>();
for (IntTaggedWord iTW : seenCounter.keySet()) {
if (iTW.word() == nullWord && iTW.tag() != nullTag) {
tags.add(iTW);
}
}
// tags for unknown words
if (DEBUG_LEXICON) {
System.err.println(
"Lexicon initializing tags for UNKNOWN WORD ("
+ Lexicon.UNKNOWN_WORD
+ ", "
+ unkWord
+ ')');
}
if (DEBUG_LEXICON) System.err.println("unSeenCounter is: " + uwModel.unSeenCounter());
if (DEBUG_LEXICON)
System.err.println(
"Train.openClassTypesThreshold is " + trainOptions.openClassTypesThreshold);
for (IntTaggedWord iT : tags) {
if (DEBUG_LEXICON)
System.err.println("Entry for " + iT + " is " + uwModel.unSeenCounter().getCount(iT));
double types = uwModel.unSeenCounter().getCount(iT);
if (types > trainOptions.openClassTypesThreshold) {
// Number of types before it's treated as open class
IntTaggedWord iTW = new IntTaggedWord(unkWord, iT.tag);
rulesWithWord[iTW.word].add(iTW);
}
}
if (testOptions.verbose || DEBUG_LEXICON) {
System.err.print("The " + rulesWithWord[unkWord].size() + " open class tags are: [");
for (IntTaggedWord item : rulesWithWord[unkWord]) {
System.err.print(" " + tagIndex.get(item.tag()));
if (DEBUG_LEXICON) {
IntTaggedWord iTprint = new IntTaggedWord(nullWord, item.tag);
System.err.print(
" (tag "
+ item.tag()
+ ", type count is "
+ uwModel.unSeenCounter().getCount(iTprint)
+ ')');
}
}
System.err.println(" ] ");
}
for (IntTaggedWord iTW : seenCounter.keySet()) {
if (iTW.tag() != nullTag && iTW.word() != nullWord) {
rulesWithWord[iTW.word].add(iTW);
}
}
}
/**
* Do max language model markov segmentation. Note that this algorithm inherently tags words as it
* goes, but that we throw away the tags in the final result so that the segmented words are
* untagged. (Note: for a couple of years till Aug 2007, a tagged result was returned, but this
* messed up the parser, because it could use no tagging but the given tagging, which often wasn't
* very good. Or in particular it was a subcategorized tagging which never worked with the current
* forceTags option which assumes that gold taggings are inherently basic taggings.)
*
* @param s A String to segment
* @return The list of segmented words.
*/
private ArrayList<HasWord> segmentWordsWithMarkov(String s) {
int length = s.length();
// Set<String> POSes = (Set<String>) POSDistribution.keySet(); // 1.5
int numTags = POSes.size();
// score of span with initial word of this tag
double[][][] scores = new double[length][length + 1][numTags];
// best (length of) first word for this span with this tag
int[][][] splitBacktrace = new int[length][length + 1][numTags];
// best tag for second word over this span, if first is this tag
int[][][] POSbacktrace = new int[length][length + 1][numTags];
for (int i = 0; i < length; i++) {
for (int j = 0; j < length + 1; j++) {
Arrays.fill(scores[i][j], Double.NEGATIVE_INFINITY);
}
}
// first fill in word probabilities
for (int diff = 1; diff <= 10; diff++) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
StringBuilder wordBuf = new StringBuilder();
for (int pos = start; pos < end; pos++) {
wordBuf.append(s.charAt(pos));
}
String word = wordBuf.toString();
for (String tag : POSes) {
IntTaggedWord itw = new IntTaggedWord(word, tag, wordIndex, tagIndex);
double score = lex.score(itw, 0, word, null);
if (start == 0) {
score += Math.log(initialPOSDist.probabilityOf(tag));
}
scores[start][end][itw.tag()] = score;
splitBacktrace[start][end][itw.tag()] = end;
}
}
}
// now fill in word combination probabilities
for (int diff = 2; diff <= length; diff++) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
for (int split = start + 1; split < end && split - start <= 10; split++) {
for (String tag : POSes) {
int tagNum = tagIndex.indexOf(tag, true);
if (splitBacktrace[start][split][tagNum] != split) {
continue;
}
Distribution<String> rTagDist = markovPOSDists.get(tag);
if (rTagDist == null) {
continue; // this happens with "*" POS
}
for (String rTag : POSes) {
int rTagNum = tagIndex.indexOf(rTag, true);
double newScore =
scores[start][split][tagNum]
+ scores[split][end][rTagNum]
+ Math.log(rTagDist.probabilityOf(rTag));
if (newScore > scores[start][end][tagNum]) {
scores[start][end][tagNum] = newScore;
splitBacktrace[start][end][tagNum] = split;
POSbacktrace[start][end][tagNum] = rTagNum;
}
}
}
}
}
}
int nextPOS = ArrayMath.argmax(scores[0][length]);
ArrayList<HasWord> words = new ArrayList<HasWord>();
int start = 0;
while (start < length) {
int split = splitBacktrace[start][length][nextPOS];
StringBuilder wordBuf = new StringBuilder();
for (int i = start; i < split; i++) {
wordBuf.append(s.charAt(i));
}
String word = wordBuf.toString();
// String tag = tagIndex.get(nextPOS);
// words.add(new TaggedWord(word, tag));
words.add(new Word(word));
if (split < length) {
nextPOS = POSbacktrace[start][length][nextPOS];
}
start = split;
}
return words;
}
// CDM 2007: I wonder what this does differently from segmentWordsWithMarkov???
private ArrayList<TaggedWord> basicSegmentWords(String s) {
int length = s.length();
// Set<String> POSes = (Set<String>) POSDistribution.keySet(); // 1.5
// best score of span
double[][] scores = new double[length][length + 1];
// best (last index of) first word for this span
int[][] splitBacktrace = new int[length][length + 1];
// best tag for word over this span
int[][] POSbacktrace = new int[length][length + 1];
for (int i = 0; i < length; i++) {
Arrays.fill(scores[i], Double.NEGATIVE_INFINITY);
}
// first fill in word probabilities
for (int diff = 1; diff <= 10; diff++) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
StringBuilder wordBuf = new StringBuilder();
for (int pos = start; pos < end; pos++) {
wordBuf.append(s.charAt(pos));
}
String word = wordBuf.toString();
// for (String tag : POSes) { // 1.5
for (Iterator<String> iter = POSes.iterator(); iter.hasNext(); ) {
String tag = iter.next();
IntTaggedWord itw = new IntTaggedWord(word, tag, wordIndex, tagIndex);
double newScore =
lex.score(itw, 0, word, null) + Math.log(lex.getPOSDistribution().probabilityOf(tag));
if (newScore > scores[start][end]) {
scores[start][end] = newScore;
splitBacktrace[start][end] = end;
POSbacktrace[start][end] = itw.tag();
}
}
}
}
// now fill in word combination probabilities
for (int diff = 2; diff <= length; diff++) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
for (int split = start + 1; split < end && split - start <= 10; split++) {
if (splitBacktrace[start][split] != split) {
continue; // only consider words on left
}
double newScore = scores[start][split] + scores[split][end];
if (newScore > scores[start][end]) {
scores[start][end] = newScore;
splitBacktrace[start][end] = split;
}
}
}
}
List<TaggedWord> words = new ArrayList<TaggedWord>();
int start = 0;
while (start < length) {
int end = splitBacktrace[start][length];
StringBuilder wordBuf = new StringBuilder();
for (int pos = start; pos < end; pos++) {
wordBuf.append(s.charAt(pos));
}
String word = wordBuf.toString();
String tag = tagIndex.get(POSbacktrace[start][end]);
words.add(new TaggedWord(word, tag));
start = end;
}
return new ArrayList<TaggedWord>(words);
}
