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The Berkeley parser analyzes the grammatical structure of natural language using probabilistic context-free grammars (PCFGs).
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package edu.berkeley.nlp.PCFGLA;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.ObjectOutputStream;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.zip.GZIPOutputStream;
import edu.berkeley.nlp.math.DoubleArrays;
import edu.berkeley.nlp.syntax.StateSet;
import edu.berkeley.nlp.syntax.Tree;
import edu.berkeley.nlp.util.ArrayUtil;
import edu.berkeley.nlp.util.Numberer;
import edu.berkeley.nlp.util.ScalingTools;
/**
*
* @author Slav Petrov
*
* SHOULD BE CLEANED UP!!!
* AND PROBABLY ALSO RENAMED SINCE IT CAN COMPUTE VITERBI PARSES AS WELL
*
* An extension of ConstrainedArrayParser that computes the scores P(w_{i:j}|A), whose
* computation involves a sum, rather than the Viterbi scores, which involve a max.
* This is used by the Labelled Recall parser (maximizes the expected number of correct
* symbols) and the Max-Rule parser (maximizes the expected number of correct rules, ie
* all 3 symbols correct).
*
*/
public class CoarseToFineMaxRuleParser extends ConstrainedArrayParser{
boolean[][][][] allowedSubStates;
boolean[][][] allowedStates;
boolean[][] vAllowedStates;
double[][] spanMass;
// allowedStates[start][end][state][0] -> is this category allowed
// allowedStates[start][end][state][i+1] -> is subcategory i allowed
Grammar[] grammarCascade;
Lexicon[] lexiconCascade;
int[][][] lChildMap;
int[][][] rChildMap;
int startLevel;
int endLevel;
// protected double[][][][] iScore;
/** outside scores; start idx, end idx, state -> logProb */
// protected double[][][][] oScore;
// protected short[] numSubStatesArray;
double[] maxThresholds;
double logLikelihood;
Tree bestTree;
boolean isBaseline;
protected final boolean doVariational;
// inside scores
protected double[][][] viScore; // start idx, end idx, state -> logProb
protected double[][][] voScore; // start idx, end idx, state -> logProb
// maxcScore does not have substate information since these are marginalized out
protected double savedScore;
protected double[][][] maxcScore; // start, end, state --> logProb
protected double[][][] maxsScore; // start, end, state --> logProb
protected int[][][] maxcSplit; // start, end, state -> split position
protected int[][][] maxcChild; // start, end, state -> unary child (if any)
protected int[][][] maxcLeftChild; // start, end, state -> left child
protected int[][][] maxcRightChild; // start, end, state -> right child
protected double unaryPenalty;
int nLevels;
final boolean[] grammarTags;
final boolean viterbiParse;
final boolean outputSub;
final boolean outputScore;
Numberer wordNumberer = Numberer.getGlobalNumberer("words");
final boolean accurate;
final boolean useGoldPOS;
double[] unscaledScoresToAdd;
ArrayParser llParser;
List posteriorsToDump;
// double edgesTouched;
// int sentencesParsed;
CoarseToFineMaxRuleParser(){
grammarTags = null;
viterbiParse = false;
outputSub = false;
outputScore = false;
accurate = false;
useGoldPOS = false;
doVariational = false;
}
public CoarseToFineMaxRuleParser(Grammar gr, Lexicon lex, double unaryPenalty, int endL,
boolean viterbi, boolean sub, boolean score, boolean accurate, boolean variational,
boolean useGoldPOS, boolean initializeCascade) {
grammar=gr;
lexicon=lex;
// this.numSubStatesArray = gr.numSubStates.clone();
//System.out.println("The unary penalty for parsing is "+unaryPenalty+".");
this.unaryPenalty = unaryPenalty;
this.accurate = accurate;
this.viterbiParse = viterbi;
this.outputScore = score;
this.outputSub = sub;
this.doVariational = variational;
this.useGoldPOS = useGoldPOS;
totalUsedUnaries=0;
nTimesRestoredUnaries=0;
nRules=0;
nRulesInf=0;
this.tagNumberer = Numberer.getGlobalNumberer("tags");
this.numStates = gr.numStates;
this.maxNSubStates = maxSubStates(gr);
this.idxC = new int[maxNSubStates];
this.scoresToAdd = new double[maxNSubStates];
this.unscaledScoresToAdd = new double[maxNSubStates];
this.grammarTags = new boolean[numStates];
for (int i=0; iendLevel){
tmpGrammar = gr;
tmpLexicon = lex;
}
else /*if (level>0&& level0) {
lChildMap[level+startLevel] = curLChildMap;
rChildMap[level+startLevel] = curRChildMap;
gr.computeReverseSubstateMapping(level,curLChildMap,curRChildMap);
}
}
tmpGrammar.splitRules();
double filter = 1.0e-4;
if (level>=0 && level=endLevel){
tmpGrammar.removeUnlikelyRules(1.0e-10,1.0);
tmpLexicon.removeUnlikelyTags(1.0e-10,1.0);
}
//System.out.println(baseGrammar.toString());
// DumpGrammar.dumpGrammar("wsj_"+level+".gr", tmpGrammar, (SophisticatedLexicon)tmpLexicon);
if (level<=endLevel || viterbiParse){
tmpGrammar.logarithmMode();
tmpLexicon.logarithmMode();
}
grammarCascade[level-startLevel]=tmpGrammar;
lexiconCascade[level-startLevel]=tmpLexicon;
}
}
void doConstrainedInsideScores(Grammar grammar, boolean viterbi, boolean logScores) {
if (!viterbi && logScores) throw new Error("This would require logAdds and is slow. Exponentiate the scores instead.");
short[] numSubStatesArray = grammar.numSubStates;
double initVal = (logScores) ? Double.NEGATIVE_INFINITY : 0;
for (int diff = 1; diff <= length; diff++) {
for (int start = 0; start < (length - diff + 1); start++) {
int end = start + diff;
for (int pState=0; pState= narrowR); // can this right constituent fit next to the left constituent?
if (!iPossibleR) { continue; }
int min1 = narrowR;
int min2 = wideLExtent[end][rState];
int min = (min1 > min2 ? min1 : min2); // can this right constituent stretch far enough to reach the left constituent?
if (min > narrowL) { continue; }
int max1 = wideRExtent[start][lState];
int max2 = narrowL;
final int max = (max1 < max2 ? max1 : max2); // can this left constituent stretch far enough to reach the right constituent?
if (min > max) { continue; }
// TODO switch order of loops for efficiency
double[][][] scores = r.getScores2();
final int nLeftChildStates = numSubStatesArray[lState];
final int nRightChildStates = numSubStatesArray[rState];
for (int split = min; split <= max; split++) {
if (!allowedStates[start][split][lState]) continue;
if (!allowedStates[split][end][rState]) continue;
for (int lp = 0; lp < nLeftChildStates; lp++) {
//if (iScore[start][split][lState] == null) continue;
//if (!allowedSubStates[start][split][lState][lp]) continue;
double lS = iScore[start][split][lState][lp];
if (lS == initVal) continue;
for (int rp = 0; rp < nRightChildStates; rp++) {
if (scores[lp][rp]==null) continue;
double rS = iScore[split][end][rState][rp];
if (rS == initVal) continue;
for (int np = 0; np < nParentStates; np++) {
if (!allowedSubStates[start][end][pState][np]) continue;
// if (level==endLevel-1) edgesTouched++;
double pS = scores[lp][rp][np];
if (pS==initVal) continue;
double thisRound = (logScores) ? pS+lS+rS : pS*lS*rS;
if (viterbi) scoresToAdd[np] = Math.max(thisRound, scoresToAdd[np]);
else scoresToAdd[np] += thisRound;
somethingChanged = true;
}
}
}
}
}
if (!somethingChanged) continue;
for (int np = 0; np < nParentStates; np++) {
if (scoresToAdd[np] > initVal) {
iScore[start][end][pState][np] = scoresToAdd[np];
}
}
if (true){//firstTime) {
if (start > narrowLExtent[end][pState]) {
narrowLExtent[end][pState] = start;
wideLExtent[end][pState] = start;
} else {
if (start < wideLExtent[end][pState]) {
wideLExtent[end][pState] = start;
}
}
if (end < narrowRExtent[start][pState]) {
narrowRExtent[start][pState] = end;
wideRExtent[start][pState] = end;
} else {
if (end > wideRExtent[start][pState]) {
wideRExtent[start][pState] = end;
}
}
}
}
double[][] scoresAfterUnaries = new double[numStates][];
boolean somethingChanged = false;
for (int pState=0; pState initVal) {
if (viterbi) iScore[start][end][pState][np] = Math.max(iScore[start][end][pState][np], thisCell[np]);
else iScore[start][end][pState][np] = iScore[start][end][pState][np] + thisCell[np];
}
}
if (true){
if (start > narrowLExtent[end][pState]) {
narrowLExtent[end][pState] = start;
wideLExtent[end][pState] = start;
} else {
if (start < wideLExtent[end][pState]) {
wideLExtent[end][pState] = start;
}
}
if (end < narrowRExtent[start][pState]) {
narrowRExtent[start][pState] = end;
wideRExtent[start][pState] = end;
} else {
if (end > wideRExtent[start][pState]) {
wideRExtent[start][pState] = end;
}
}
}
}
}
}
}
/** Fills in the oScore array of each category over each span
* of length 2 or more. This version computes the posterior
* outside scores, not the Viterbi outside scores.
*/
void doConstrainedOutsideScores(Grammar grammar, boolean viterbi, boolean logScores) {
short[] numSubStatesArray = grammar.numSubStates;
double initVal = (logScores) ? Double.NEGATIVE_INFINITY : 0.0;
for (int diff = length; diff >= 1; diff--) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
// do unaries
double[][] scoresAfterUnaries = new double[numStates][];
boolean somethingChanged = false;
for (int cState=0; cState1 && !grammar.isGrammarTag[cState]) continue;
if (oScore[start][end][cState] == null) { continue; }
UnaryRule[] rules = null;
if (viterbi) rules = grammar.getClosedViterbiUnaryRulesByChild(cState);
else rules = grammar.getClosedSumUnaryRulesByChild(cState);
final int nChildStates = numSubStatesArray[cState];
final int numRules = rules.length;
for (int r = 0; r < numRules; r++) {
UnaryRule ur = rules[r];
int pState = ur.parentState;
if ((pState == cState)) continue;// && (np == cp))continue;
if (oScore[start][end][pState] == null) { continue; }
double[][] scores = ur.getScores2();
final int nParentStates = numSubStatesArray[pState];
for (int cp = 0; cp < nChildStates; cp++) {
if (scores[cp]==null) continue;
if (!allowedSubStates[start][end][cState][cp]) continue;
for (int np = 0; np < nParentStates; np++) {
double pS = scores[cp][np];
if (pS == initVal) continue;
double oS = oScore[start][end][pState][np];
if (oS == initVal) continue;
double thisRound = (logScores) ? oS+pS : oS*pS;
if (scoresAfterUnaries[cState]==null){
scoresAfterUnaries[cState] = new double[numSubStatesArray[cState]];
if (viterbi) Arrays.fill(scoresAfterUnaries[cState], initVal);
}
if (viterbi) scoresAfterUnaries[cState][cp] = Math.max(thisRound, scoresAfterUnaries[cState][cp]);
else scoresAfterUnaries[cState][cp] += thisRound;
somethingChanged = true;
}
}
}
}
if (somethingChanged){
for (int cState=0; cState initVal){
if (viterbi) oScore[start][end][cState][cp] = Math.max(oScore[start][end][cState][cp], thisCell[cp]);
else oScore[start][end][cState][cp] += thisCell[cp];
}
}
}
}
// do binaries
for (int pState=0; pState 2) {
int min2 = wideLExtent[end][rState];
min = (min1 > min2 ? min1 : min2);
if (max1 < min) { continue; }
int max2 = wideRExtent[start][lState];
max = (max1 < max2 ? max1 : max2);
if (max < min) { continue; }
}
double[][][] scores = br.getScores2();
final int nLeftChildStates = numSubStatesArray[lState];
final int nRightChildStates = numSubStatesArray[rState];
for (int split = min; split <= max; split++) {
if (oScore[start][split][lState] == null) continue;
if (oScore[split][end][rState] == null) continue;
//if (!allowedStates[start][split][lState]) continue;
//if (!allowedStates[split][end][rState]) continue;
double[] rightScores = new double[nRightChildStates];
if (viterbi) Arrays.fill(rightScores,initVal);
Arrays.fill(scoresToAdd,initVal);
somethingChanged = false;
for (int lp=0; lp initVal){
if (viterbi) oScore[start][split][lState][cp] = Math.max(oScore[start][split][lState][cp], scoresToAdd[cp]);
else oScore[start][split][lState][cp] += scoresToAdd[cp];
}
}
for (int cp=0; cp initVal) {
if (viterbi) oScore[split][end][rState][cp] = Math.max(oScore[split][end][rState][cp], rightScores[cp]);
else oScore[split][end][rState][cp] += rightScores[cp];
}
}
}
}
}
}
}
}
void initializeChart(List sentence, Lexicon lexicon,boolean noSubstates,
boolean noSmoothing,List posTags, boolean scale) {
int start = 0;
int end = start+1;
for (String word : sentence) {
end = start+1;
int goldTag = -1;
if (useGoldPOS && posTags!=null) {
goldTag = tagNumberer.number(posTags.get(start));
}
for (int tag=0; tag start, etc.)
// System.out.println("initializing iScore arrays with length " + length + " and numStates " + numStates);
//if (logProbs){
viScore = new double[length][length + 1][];
voScore = new double[length][length + 1][];
//} else{
iScore = new double[length][length + 1][][];
oScore = new double[length][length + 1][][];
//iScale = new int[length][length + 1][];
//oScale = new int[length][length + 1][];
//}
allowedSubStates = new boolean[length][length+1][][];
allowedStates = new boolean[length][length+1][];
vAllowedStates = new boolean[length][length+1];
}
for (int start = 0; start < length; start++) {
for (int end = start + 1; end <= length; end++) {
if (firstTime){
viScore[start][end] = new double[numStates];
voScore[start][end] = new double[numStates];
iScore[start][end] = new double[numStates][];
oScore[start][end] = new double[numStates][];
//iScale[start][end] = new int[numStates];
//oScale[start][end] = new int[numStates];
allowedSubStates[start][end] = new boolean[numStates][];
allowedStates[start][end] = grammarTags.clone();
//Arrays.fill(allowedStates[start][end], true);
vAllowedStates[start][end] = true;
}
for (int state=0; state1 && !grammarTags[state]) continue;
/*if (refreshOnly){
if (allowedStates[start][end][state]){
Arrays.fill(iScore[start][end][state], 0);
Arrays.fill(oScore[start][end][state], 0);
}
continue;
}*/
if (firstTime || allowedStates[start][end][state]){
if (level<1){
viScore[start][end][state] = Double.NEGATIVE_INFINITY;
voScore[start][end][state] = Double.NEGATIVE_INFINITY;
} else{
iScore[start][end][state] = new double[numSubStatesArray[state]];
oScore[start][end][state] = new double[numSubStatesArray[state]];
Arrays.fill(iScore[start][end][state], initVal);
Arrays.fill(oScore[start][end][state], initVal);
//Arrays.fill(iScale[start][end], Integer.MIN_VALUE);
//Arrays.fill(oScale[start][end], Integer.MIN_VALUE);
boolean[] newAllowedSubStates = new boolean[numSubStatesArray[state]];
if (allowedSubStates[start][end][state]==null){
Arrays.fill(newAllowedSubStates,true);
allowedSubStates[start][end][state] = newAllowedSubStates;
} else{
if (!justInit){
int[][] curLChildMap = lChildMap[level-2];
int[][] curRChildMap = rChildMap[level-2];
for (int i=0; i0 && start==0 && end==length ) {
if (iScore[start][end][0]==null)
System.out.println("ROOT does not span the entire tree!");
}
}
}
narrowRExtent = new int[length + 1][numStates];
wideRExtent = new int[length + 1][numStates];
narrowLExtent = new int[length + 1][numStates];
wideLExtent = new int[length + 1][numStates];
for (int loc = 0; loc <= length; loc++) {
Arrays.fill(narrowLExtent[loc], -1); // the rightmost left with state s ending at i that we can get is the beginning
Arrays.fill(wideLExtent[loc], length + 1); // the leftmost left with state s ending at i that we can get is the end
Arrays.fill(narrowRExtent[loc], length + 1); // the leftmost right with state s starting at i that we can get is the end
Arrays.fill(wideRExtent[loc], -1); // the rightmost right with state s starting at i that we can get is the beginning
}
iScale = null;
oScale = null;
}
protected void clearArrays() {
iScore = oScore = null;
viScore = voScore = null;
allowedSubStates = null;
vAllowedStates = null;
// iPossibleByL = iPossibleByR = oFilteredEnd = oFilteredStart =
// oPossibleByL = oPossibleByR = tags = null;
narrowRExtent = wideRExtent = narrowLExtent = wideLExtent = null;
}
protected void pruneChart(double threshold, short[] numSubStatesArray, int level){
int totalStates = 0, previouslyPossible = 0, nowPossible = 0;
//threshold = Double.NEGATIVE_INFINITY;
double sentenceProb = (level<1) ? viScore[0][length][0] : iScore[0][length][0][0];
//double sentenceScale = iScale[0][length][0];//+1.0 for oScale
if (level<1) nowPossible=totalStates=previouslyPossible=length;
int startDiff = (level<0) ? 2 : 1;
for (int diff = startDiff; diff <= length; diff++) {
for (int start = 0; start < (length - diff + 1); start++) {
int end = start + diff;
int lastState = (level<0) ? 1 : numSubStatesArray.length;
for (int state = 0; state < lastState; state++) {
if (diff>1&&!grammarTags[state]) continue;
//boolean allFalse = true;
if (state==0){
allowedStates[start][end][state]=true;
// if (level>1){
// allowedSubStates[start][end][state] = new boolean[1];
// allowedSubStates[start][end][state][0] = true;
// }
continue;
}
if (level==0){
if (!vAllowedStates[start][end]) {
allowedStates[start][end][state]=false;
totalStates++;
continue;
}
} else if (level>0){
if (!allowedStates[start][end][state]) {
totalStates+=numSubStatesArray[state];
continue;
}
}
if (level<1){
totalStates++;
previouslyPossible++;
double iS = viScore[start][end][state];
double oS = voScore[start][end][state];
if (iS==Double.NEGATIVE_INFINITY||oS==Double.NEGATIVE_INFINITY) {
if (level==0) allowedStates[start][end][state] = false;
else /*level==-1*/ vAllowedStates[start][end]=false;
continue;
}
double posterior = iS + oS - sentenceProb;
if (posterior > threshold) {
// spanMass[start][end]+=Math.exp(posterior);
if (level==0) allowedStates[start][end][state]=true;
else vAllowedStates[start][end]=true;
nowPossible++;
} else {
if (level==0) allowedStates[start][end][state] = false;
else vAllowedStates[start][end]=false;
}
continue;
}
// level >= 1 -> iterate over substates
boolean nonePossible = true;
for (int substate = 0; substate < numSubStatesArray[state]; substate++) {
totalStates++;
if (!allowedSubStates[start][end][state][substate]) continue;
previouslyPossible++;
double iS = iScore[start][end][state][substate];
double oS = oScore[start][end][state][substate];
if (iS==Double.NEGATIVE_INFINITY||oS==Double.NEGATIVE_INFINITY) {
allowedSubStates[start][end][state][substate] = false;
continue;
}
double posterior = iS + oS - sentenceProb;
if (posterior > threshold) {
allowedSubStates[start][end][state][substate]=true;
nowPossible++;
// spanMass[start][end]+=Math.exp(posterior);
nonePossible=false;
} else {
allowedSubStates[start][end][state][substate] = false;
}
/*if (thisScale>sentenceScale){
posterior *= Math.pow(GrammarTrainer.SCALE,thisScale-sentenceScale);
}*/
//}
//allowedStates[start][end][state][0] = !allFalse;
//int thisScale = iScale[start][end][state]+oScale[start][end][state];
/*if (sentenceScale>thisScale){
// too small anyways
allowedStates[start][end][state][0] = false;
continue;
}*/
}
if (nonePossible) allowedStates[start][end][state]=false;
}
}
}
// System.out.print("[");
// for(int st=0; st sentence, Tree tree,boolean noSmoothing,List posTags){
boolean keepGoldAlive = (tree!=null); // we are given the gold tree -> make sure we don't prune it away
clearArrays();
length = (short)sentence.size();
double score = 0;
Grammar curGrammar = null;
Lexicon curLexicon = null;
double[] accurateThresholds = {-8,-12,-12,-11,-12,-12,-14,-14};
// double[] accurateThresholds = {-10,-14,-14,-14,-14,-14,-16,-16};
double[] fastThresholds = {-8,-9.75,-10,-9.6,-9.66,-8.01,-7.4,-10,-10};
// double[] accurateThresholds = {-8,-9,-9,-9,-9,-9,-10};
// double[] fastThresholds = {-2,-8,-9,-8,-8,-7.5,-7,-8};
double[] pruningThreshold = null;
if (accurate)
pruningThreshold = accurateThresholds;
else
pruningThreshold = fastThresholds;
//int startLevel = -1;
for (level=startLevel; level<=endLevel; level++){
if (level==-1) continue; // don't do the pre-pre parse
if (!isBaseline && level==endLevel) continue;//
curGrammar = grammarCascade[level-startLevel];
curLexicon = lexiconCascade[level-startLevel];
// createArrays(level==startLevel,curGrammar.numStates,curGrammar.numSubStates,level,Double.NEGATIVE_INFINITY,false);
createArrays(level==0,curGrammar.numStates,curGrammar.numSubStates,level,Double.NEGATIVE_INFINITY,false);
initializeChart(sentence,curLexicon,level<1,noSmoothing,posTags,false);
final boolean viterbi = true, logScores = true;
if (level<1){
doConstrainedViterbiInsideScores(curGrammar,level==startLevel);
score = viScore[0][length][0];
} else {
doConstrainedInsideScores(curGrammar,viterbi,logScores);
score = iScore[0][length][0][0];
}
if (score==Double.NEGATIVE_INFINITY) continue;
// System.out.println("\nFound a parse for sentence with length "+length+". The LL is "+score+".");
if (level<1){
voScore[0][length][0] = 0.0;
doConstrainedViterbiOutsideScores(curGrammar,level==startLevel);
} else {
oScore[0][length][0][0] = 0.0;
doConstrainedOutsideScores(curGrammar,viterbi,logScores);
}
pruneChart(/*Double.NEGATIVE_INFINITY*/pruningThreshold[level+1], curGrammar.numSubStates, level);
if (keepGoldAlive) ensureGoldTreeSurvives(tree, level);
}
}
protected void ensureGoldTreeSurvives(Tree tree, int level){
List> children = tree.getChildren();
for (Tree child : children) {
if (!child.isLeaf())
ensureGoldTreeSurvives(child,level);
}
StateSet node = tree.getLabel();
short state = node.getState();
if (level<0){
vAllowedStates[node.from][node.to]=true;
}
else{
int start = node.from, end = node.to;
/*if (end-start==1 && !grammarTags[state]){ // POS tags -> use gold ones until lexicon is updated
allowedStates[start][end]= new boolean[numStates];
Arrays.fill(allowedStates[start][end], false);
allowedSubStates[start][end] = new boolean[numStates][];
}*/
allowedStates[start][end][state]=true;
if (allowedSubStates[start][end]==null) allowedSubStates[start][end] = new boolean[numStates][];
allowedSubStates[start][end][state] = null; // will be taken care of in createArrays
//boolean[] newArray = new boolean[numSubStatesArray[state]+1];
//Arrays.fill(newArray, true);
//allowedSubStates[node.from][node.to][state] = newArray;
}
}
private void setGoldTreeCountsToOne(Tree tree){
StateSet node = tree.getLabel();
short state = node.getState();
iScore[node.from][node.to][state][0]=1.0;
oScore[node.from][node.to][state][0]=1.0;
List> children = tree.getChildren();
for (Tree child : children) {
if (!child.isLeaf()) setGoldTreeCountsToOne(child);
}
}
public void updateFinalGrammarAndLexicon(Grammar grammar, Lexicon lexicon){
grammarCascade[endLevel-startLevel+1] = grammar;
lexiconCascade[endLevel-startLevel+1] = lexicon;
Grammar tmpGrammar = grammar.copyGrammar(false);
tmpGrammar.logarithmMode();
Lexicon tmpLexicon = lexicon.copyLexicon();
tmpLexicon.logarithmMode();
grammarCascade[endLevel-startLevel] = null;//tmpGrammar; <- since we don't pre-parse with G
lexiconCascade[endLevel-startLevel] = null;//tmpLexicon;
}
public Tree getBestParse(List sentence){
return getBestConstrainedParse(sentence, null, false);
}
public double getLogInsideScore(){
return logLikelihood;
}
public Tree getBestConstrainedParse(List sentence, List posTags, boolean[][][][] allowedS){//List[][] pStates) {
if (allowedS==null) return getBestConstrainedParse(sentence, posTags, false);
clearArrays();
length = (short)sentence.size();
Grammar curGrammar = grammarCascade[endLevel-startLevel+1];
Lexicon curLexicon = lexiconCascade[endLevel-startLevel+1];
double initVal = (viterbiParse) ? Double.NEGATIVE_INFINITY : 0;
int level = isBaseline ? 1 : endLevel;
allowedSubStates = allowedS;
createArrays(true,curGrammar.numStates,curGrammar.numSubStates,level,initVal,false);
setConstraints(allowedS);
return getBestConstrainedParse(sentence, posTags, true);
}
/**
* @param allowedS
*/
private void setConstraints(boolean[][][][] allowedS) {
allowedSubStates = allowedS;
for (int start = 0; start < length; start++) {
for (int end = start + 1; end <= length; end++) {
for (int state=0; state getBestConstrainedParse(List sentence, List posTags, boolean noPreparse) {
if (sentence.size()==0) return new Tree("ROOT");
if (!noPreparse) doPreParses(sentence,null,false,posTags);
bestTree = new Tree("ROOT");
double score = 0;
Grammar curGrammar = grammarCascade[endLevel-startLevel+1];
Lexicon curLexicon = lexiconCascade[endLevel-startLevel+1];
//numSubStatesArray = grammar.numSubStates;
//clearArrays();
double initVal = (viterbiParse) ? Double.NEGATIVE_INFINITY : 0;
int level = isBaseline ? 1 : endLevel;
createArrays(false,curGrammar.numStates,curGrammar.numSubStates,level,initVal,false);
initializeChart(sentence,curLexicon,false,false,posTags,false);
doConstrainedInsideScores(curGrammar,viterbiParse,viterbiParse);
score = iScore[0][length][0][0];
if (!viterbiParse) score = Math.log(score);// + (100*iScale[0][length][0]);
logLikelihood = score;
if (score != Double.NEGATIVE_INFINITY) {
// System.out.println("\nFinally found a parse for sentence with length "+length+". The LL is "+score+".");
if (!viterbiParse) {
oScore[0][length][0][0] = 1.0;
doConstrainedOutsideScores(curGrammar,viterbiParse,false);
doConstrainedMaxCScores(sentence,curGrammar,curLexicon,false);
}
}
else {
// System.err.println("Using scaling code for sentence with length "+length+".");
setupScaling();
initializeChart(sentence,curLexicon,false,false,posTags,true);
doScaledConstrainedInsideScores(curGrammar);
score = iScore[0][length][0][0];
if (!viterbiParse) score = Math.log(score) + (100*iScale[0][length][0]);
// System.out.println("Finally found a parse for sentence with length "+length+". The LL is "+score+".");
// System.out.println("Scale: "+iScale[0][length][0]);
oScore[0][length][0][0] = 1.0;
oScale[0][length][0] = 0;
doScaledConstrainedOutsideScores(curGrammar);
doConstrainedMaxCScores(sentence,curGrammar,curLexicon,true);
score = iScore[0][length][0][0];
if (!viterbiParse) score = Math.log(score);// + (100*iScale[0][length][0]);
}
grammar = curGrammar;
lexicon = curLexicon;
if (score != Double.NEGATIVE_INFINITY) {
if (viterbiParse) bestTree = extractBestViterbiParse(0, 0, 0, length, sentence);
else {
bestTree = extractBestMaxRuleParse(0, length, sentence);
savedScore = maxcScore[0][length][0];
}
}
// sentencesParsed++;
// System.out.println("For parsing "+sentencesParsed+" I hat to touch "+edgesTouched/((double)sentencesParsed)+" on average.");
return bestTree;
}
public double getModelScore(Tree parsedTree) {
if (viterbiParse) return logLikelihood;
return savedScore;
}
public double getConfidence(Tree tree){
if (logLikelihood == Double.NEGATIVE_INFINITY) return logLikelihood;
// try{
double treeLL = getLogLikelihood(tree);
double sentenceLL = getLogLikelihood();
return treeLL - sentenceLL;
// } catch (Exception e){
// System.err.println("Couldn't compute LL of tree: " + tree);
// return Double.NEGATIVE_INFINITY;
// }
}
public double getLogLikelihood(Tree tree){
if (logLikelihood == Double.NEGATIVE_INFINITY) return logLikelihood;
if (viterbiParse) return logLikelihood;
ArrayList> resultList = new ArrayList>();
Tree newTree = TreeAnnotations.processTree(tree,1,0,binarization,false);
resultList.add(newTree);
StateSetTreeList resultStateSetTrees = new StateSetTreeList(resultList, grammar.numSubStates, false, tagNumberer);
if (llParser==null) llParser = new ArrayParser(grammar, lexicon);
for (Tree t : resultStateSetTrees){
llParser.doInsideScores(t,false,false,null); // Only inside scores are needed here
double ll = Math.log(t.getLabel().getIScore(0));
ll += 100*t.getLabel().getIScale();
return ll;
}
return Double.NEGATIVE_INFINITY;
}
public double getLogLikelihood(){
if (logLikelihood == Double.NEGATIVE_INFINITY) return logLikelihood;
if (viterbiParse) return logLikelihood;
logLikelihood = Math.log(iScore[0][length][0][0]);// +
if (iScale != null) logLikelihood += ScalingTools.LOGSCALE*iScale[0][length][0];
return logLikelihood;
}
/** Assumes that inside and outside scores (sum version, not viterbi) have been computed.
* In particular, the narrowRExtent and other arrays need not be updated.
*/
void doConstrainedMaxCScores(List sentence, Grammar grammar, Lexicon lexicon, final boolean scale) {
short[] numSubStatesArray = grammar.numSubStates;
double initVal = Double.NEGATIVE_INFINITY;
maxcScore = new double[length][length + 1][numStates];
maxcSplit = new int[length][length + 1][numStates];
maxcChild = new int[length][length + 1][numStates];
maxcLeftChild = new int[length][length + 1][numStates];
maxcRightChild = new int[length][length + 1][numStates];
ArrayUtil.fill(maxcScore, Double.NEGATIVE_INFINITY);
double logNormalizer = iScore[0][length][0][0];
// double thresh2 = threshold*logNormalizer;
for (int diff = 1; diff <= length; diff++) {
//System.out.print(diff + " ");
for (int start = 0; start < (length - diff + 1); start++) {
int end = start + diff;
Arrays.fill(maxcSplit[start][end], -1);
Arrays.fill(maxcChild[start][end], -1);
Arrays.fill(maxcLeftChild[start][end], -1);
Arrays.fill(maxcRightChild[start][end], -1);
if (diff > 1) {
// diff > 1: Try binary rules
for (int pState=0; pState= narrowR); // can this right constituent fit next to the left constituent?
if (!iPossibleR) { continue; }
int min1 = narrowR;
int min2 = wideLExtent[end][rState];
int min = (min1 > min2 ? min1 : min2); // can this right constituent stretch far enough to reach the left constituent?
if (min > narrowL) { continue; }
int max1 = wideRExtent[start][lState];
int max2 = narrowL;
int max = (max1 < max2 ? max1 : max2); // can this left constituent stretch far enough to reach the right constituent?
if (min > max) { continue; }
double[][][] scores = r.getScores2();
int nLeftChildStates = numSubStatesArray[lState]; // == scores.length;
int nRightChildStates = numSubStatesArray[rState]; // == scores[0].length;
double scoreToBeat = maxcScore[start][end][pState];
for (int split = min; split <= max; split++) {
double ruleScore = 0;
if (!allowedStates[start][split][lState]) continue;
if (!allowedStates[split][end][rState]) continue;
double leftChildScore = maxcScore[start][split][lState];
double rightChildScore = maxcScore[split][end][rState];
if (leftChildScore==initVal||rightChildScore==initVal) continue;
double scalingFactor = 0.0;
if (scale) scalingFactor = Math.log(ScalingTools.calcScaleFactor(
oScale[start][end][pState]+iScale[start][split][lState]+
iScale[split][end][rState]-iScale[0][length][0]));
double gScore = leftChildScore + scalingFactor + rightChildScore;
if (gScore < scoreToBeat) continue; // no chance of finding a better derivation
for (int lp = 0; lp < nLeftChildStates; lp++) {
double lIS = iScore[start][split][lState][lp];
if (lIS == 0) continue;
// if (lIS < thresh2) continue;
//if (!allowedSubStates[start][split][lState][lp]) continue;
for (int rp = 0; rp < nRightChildStates; rp++) {
if (scores[lp][rp]==null) continue;
double rIS = iScore[split][end][rState][rp];
if (rIS == 0) continue;
// if (rIS < thresh2) continue;
//if (!allowedSubStates[split][end][rState][rp]) continue;
for (int np = 0; np < nParentStates; np++) {
//if (!allowedSubStates[start][end][pState][np]) continue;
double pOS = oScore[start][end][pState][np];
if (pOS == 0) continue;
// if (pOS < thresh2) continue;
double ruleS = scores[lp][rp][np];
if (ruleS == 0) continue;
ruleScore += (pOS * ruleS * lIS * rIS) / logNormalizer;
}
}
}
if (ruleScore==0) continue;
if (doVariational){
double norm = 0;
for (int np = 0; np < nParentStates; np++) {
norm += oScore[start][end][pState][np]/logNormalizer*iScore[start][end][pState][np];
}
ruleScore /= norm;
}
// double gScore = ruleScore * leftChildScore * scalingFactor * rightChildScore;
gScore += Math.log(ruleScore);
if (gScore > scoreToBeat) {
scoreToBeat = gScore;
maxcScore[start][end][pState] = gScore;
maxcSplit[start][end][pState] = split;
maxcLeftChild[start][end][pState] = lState;
maxcRightChild[start][end][pState] = rState;
}
}
}
}
} else { // diff == 1
// We treat TAG --> word exactly as if it was a unary rule, except the score of the rule is
// given by the lexicon rather than the grammar and that we allow another unary on top of it.
//for (int tag : lexicon.getAllTags()){
for (int tag=0; tag maxcScoreStartEnd[pState]) {
maxcScoreStartEnd[pState] = gScore;
maxcChild[start][end][pState] = cState;
}
}
}
// for (int i = 0; i < numStates; i++) {
// if (maxcScore[start][end][i]+(1-unaryBonus[i]) > maxcScoreStartEnd[i]){
// maxcScore[start][end][i]+=(1-unaryBonus[i]);
// } else {
// maxcScore[start][end][i] = maxcScoreStartEnd[i];
// maxcChild[start][end][i] = unaryChild[i];
// }
// }
if (foundOne&&doVariational) maxcScoreStartEnd = closeVariationalRules(ruleScores,start,end);
maxcScore[start][end] = maxcScoreStartEnd;
}
}
}
/**
* Returns the best parse, the one with maximum expected labelled recall.
* Assumes that the maxc* arrays have been filled.
*/
public Tree extractBestMaxRuleParse(int start, int end, List sentence ) {
return extractBestMaxRuleParse1(start, end, 0, sentence);
}
/**
* Returns the best parse for state "state", potentially starting with a unary rule
*/
public Tree extractBestMaxRuleParse1(int start, int end, int state, List sentence ) {
//System.out.println(start+", "+end+";");
int cState = maxcChild[start][end][state];
if (cState == -1) {
return extractBestMaxRuleParse2(start, end, state, sentence);
} else {
List> child = new ArrayList>();
child.add( extractBestMaxRuleParse2(start, end, cState, sentence) );
String stateStr = (String) tagNumberer.object(state);
if (stateStr.endsWith("^g")) stateStr = stateStr.substring(0,stateStr.length()-2);
totalUsedUnaries++;
//System.out.println("Adding a unary spanning from "+start+" to "+end+". P: "+stateStr+" C: "+child.get(0).getLabel());
int intermediateNode = grammar.getUnaryIntermediate((short)state,(short)cState);
// if (intermediateNode==0){
// System.out.println("Added a bad unary from "+start+" to "+end+". P: "+stateStr+" C: "+child.get(0).getLabel());
// }
if (intermediateNode>0){
List> restoredChild = new ArrayList>();
nTimesRestoredUnaries++;
String stateStr2 = (String)tagNumberer.object(intermediateNode);
if (stateStr2.endsWith("^g")) stateStr2 = stateStr2.substring(0,stateStr2.length()-2);
restoredChild.add(new Tree(stateStr2, child));
//System.out.println("Restored a unary from "+start+" to "+end+": "+stateStr+" -> "+stateStr2+" -> "+child.get(0).getLabel());
return new Tree(stateStr,restoredChild);
}
return new Tree(stateStr, child);
}
}
/**
* Returns the best parse for state "state", but cannot start with a unary
*/
public Tree extractBestMaxRuleParse2(int start, int end, int state, List sentence ) {
List> children = new ArrayList>();
String stateStr = (String)tagNumberer.object(state);//+""+start+""+end;
if (stateStr.endsWith("^g")) stateStr = stateStr.substring(0,stateStr.length()-2);
boolean posLevel = (end - start == 1);
if (posLevel) {
if (grammar.isGrammarTag(state)){
List> childs = new ArrayList>();
childs.add(new Tree(sentence.get(start)));
String stateStr2 = (String)tagNumberer.object(maxcChild[start][end][state]);//+""+start+""+end;
children.add(new Tree(stateStr2,childs));
}
else children.add(new Tree(sentence.get(start)));
} else {
int split = maxcSplit[start][end][state];
if (split == -1) {
System.err.println("Warning: no symbol can generate the span from "+ start+ " to "+end+".");
System.err.println("The score is "+maxcScore[start][end][state]+" and the state is supposed to be "+stateStr);
System.err.println("The insideScores are "+Arrays.toString(iScore[start][end][state])+" and the outsideScores are " +Arrays.toString(oScore[start][end][state]));
System.err.println("The maxcScore is "+maxcScore[start][end][state]);
//return extractBestMaxRuleParse2(start, end, maxcChild[start][end][state], sentence);
return new Tree("ROOT");
}
int lState = maxcLeftChild[start][end][state];
int rState = maxcRightChild[start][end][state];
Tree leftChildTree = extractBestMaxRuleParse1(start, split, lState, sentence);
Tree rightChildTree = extractBestMaxRuleParse1(split, end, rState, sentence);
children.add(leftChildTree);
children.add(rightChildTree);
}
return new Tree(stateStr, children);
}
/** Fills in the iScore array of each category over each span
* of length 2 or more.
*/
void doConstrainedViterbiInsideScores(Grammar grammar, boolean level0grammar) {
short[] numSubStatesArray = grammar.numSubStates;
//double[] oldIScores = new double[maxNSubStates];
//int smallestScale = 10, largestScale = -10;
for (int diff = 1; diff <= length; diff++) {
for (int start = 0; start < (length - diff + 1); start++) {
int end = start + diff;
final int lastState = (level0grammar) ? 1 : numSubStatesArray.length;
for (int pState=0; pState= narrowR); // can this right constituent fit next to the left constituent?
if (!iPossibleR) { continue; }
int min1 = narrowR;
int min2 = wideLExtent[end][rState];
int min = (min1 > min2 ? min1 : min2); // can this right constituent stretch far enough to reach the left constituent?
if (min > narrowL) { continue; }
int max1 = wideRExtent[start][lState];
int max2 = narrowL;
int max = (max1 < max2 ? max1 : max2); // can this left constituent stretch far enough to reach the right constituent?
if (min > max) { continue; }
// new: loop over all substates
double[][][] scores = r.getScores2();
double pS = Double.NEGATIVE_INFINITY;
if (scores[0][0]!=null) pS = scores[0][0][0];
if (pS == Double.NEGATIVE_INFINITY) continue;
for (int split = min; split <= max; split++) {
if (!vAllowedStates[start][split]) continue;
if (!vAllowedStates[split][end]) continue;
double lS = viScore[start][split][lState];
if (lS == Double.NEGATIVE_INFINITY) continue;
double rS = viScore[split][end][rState];
if (rS == Double.NEGATIVE_INFINITY) continue;
double tot = pS + lS + rS;
if (tot >= bestIScore) { bestIScore = tot;}
}
}
if (bestIScore > oldIScore) { // this way of making "parentState" is better
// than previous
viScore[start][end][pState] = bestIScore;
if (oldIScore == Double.NEGATIVE_INFINITY) {
if (start > narrowLExtent[end][pState]) {
narrowLExtent[end][pState] = start;
wideLExtent[end][pState] = start;
} else {
if (start < wideLExtent[end][pState]) {
wideLExtent[end][pState] = start;
}
}
if (end < narrowRExtent[start][pState]) {
narrowRExtent[start][pState] = end;
wideRExtent[start][pState] = end;
} else {
if (end > wideRExtent[start][pState]) {
wideRExtent[start][pState] = end;
}
}
}
}
}
final int lastStateU = (level0grammar&&diff>1) ? 1 : numSubStatesArray.length;
for (int pState=0; pState= bestIScore) { bestIScore = tot; }
}
if (bestIScore > oldIScore) {
viScore[start][end][pState] = bestIScore;
if (oldIScore == Double.NEGATIVE_INFINITY) {
if (start > narrowLExtent[end][pState]) {
narrowLExtent[end][pState] = start;
wideLExtent[end][pState] = start;
} else {
if (start < wideLExtent[end][pState]) {
wideLExtent[end][pState] = start;
}
}
if (end < narrowRExtent[start][pState]) {
narrowRExtent[start][pState] = end;
wideRExtent[start][pState] = end;
} else {
if (end > wideRExtent[start][pState]) {
wideRExtent[start][pState] = end;
}
}
}
// }
//}
}
}
}
}
}
// void doConstrainedViterbiSubstateInsideScores(Grammar grammar) {
// numSubStatesArray = grammar.numSubStates;
//
// for (int diff = 1; diff <= length; diff++) {
// for (int start = 0; start < (length - diff + 1); start++) {
// int end = start + diff;
// final int lastState = numSubStatesArray.length;
// for (int pState=0; pState= narrowR); // can this right constituent fit next to the left constituent?
// if (!iPossibleR) { continue; }
//
// int min1 = narrowR;
// int min2 = wideLExtent[end][rState];
// int min = (min1 > min2 ? min1 : min2); // can this right constituent stretch far enough to reach the left constituent?
// if (min > narrowL) { continue; }
//
// int max1 = wideRExtent[start][lState];
// int max2 = narrowL;
// int max = (max1 < max2 ? max1 : max2); // can this left constituent stretch far enough to reach the right constituent?
// if (min > max) { continue; }
//
// // new: loop over all substates
// double[][][] scores = r.getScores2();
// for (int np = 0; np < nParentSubStates; np++) {
// if (!allowedSubStates[start][end][pState][np]) continue;
// for (int split = min; split <= max; split++) {
// if (!allowedStates[start][split][lState]) continue;
// if (!allowedStates[split][end][rState]) continue;
//
// for (int lp = 0; lp < scores.length; lp++) {
// //if (!allowedSubStates[start][split][lState][lp]) continue;
// double lS = iScore[start][split][lState][lp];
// if (lS == Double.NEGATIVE_INFINITY) continue;
//
// for (int rp = 0; rp < scores[0].length; rp++) {
// //if (!allowedSubStates[split][end][rState][rp]) continue;
// double pS = Double.NEGATIVE_INFINITY;
// if (scores[lp][rp]!=null) pS = scores[lp][rp][np];
// if (pS==Double.NEGATIVE_INFINITY){
// continue;
// //System.out.println("s "+start+" sp "+split+" e "+end+" pS "+pS+" rS "+rS);
// }
// double rS = iScore[split][end][rState][rp];
// if (rS == Double.NEGATIVE_INFINITY) continue;
//
// double tot = pS + lS + rS;
// if (tot >= bestIScore[np]) { bestIScore[np] = tot;}
// }
// }
// }
// }
// }
// boolean firstTime = true;
// for (int s=0; s oldIScore[s]) { // this way of making "parentState" is better
// // than previous
// iScore[start][end][pState][s] = bestIScore[s];
// if (firstTime && oldIScore[s] == Double.NEGATIVE_INFINITY) {
// firstTime = false;
// if (start > narrowLExtent[end][pState]) {
// narrowLExtent[end][pState] = start;
// wideLExtent[end][pState] = start;
// } else {
// if (start < wideLExtent[end][pState]) {
// wideLExtent[end][pState] = start;
// }
// }
// if (end < narrowRExtent[start][pState]) {
// narrowRExtent[start][pState] = end;
// wideRExtent[start][pState] = end;
// } else {
// if (end > wideRExtent[start][pState]) {
// wideRExtent[start][pState] = end;
// }
// }
// }
// }
// }
// }
// final int lastStateU = numSubStatesArray.length;
// for (int pState=0; pState= bestIScore[np]) { bestIScore[np] = tot; }
// }
// }
// }
// boolean firstTime = true;
// for (int s=0; s oldIScore[s]) {
// iScore[start][end][pState][s] = bestIScore[s];
// if (firstTime && oldIScore[s] == Double.NEGATIVE_INFINITY) {
// firstTime = false;
// if (start > narrowLExtent[end][pState]) {
// narrowLExtent[end][pState] = start;
// wideLExtent[end][pState] = start;
// } else {
// if (start < wideLExtent[end][pState]) {
// wideLExtent[end][pState] = start;
// }
// }
// if (end < narrowRExtent[start][pState]) {
// narrowRExtent[start][pState] = end;
// wideRExtent[start][pState] = end;
// } else {
// if (end > wideRExtent[start][pState]) {
// wideRExtent[start][pState] = end;
// }
// }
// }
// }
// }
// }
// }
// }
// }
void doConstrainedViterbiOutsideScores(Grammar grammar, boolean level0grammar) {
for (int diff = length; diff >= 1; diff--) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
final int lastState = (level0grammar) ? 1 : numStates;
for (int cState=0; cState1 && !grammar.isGrammarTag[cState]) continue;
if (!vAllowedStates[start][end]) continue;
double iS = viScore[start][end][cState];
if (iS == Double.NEGATIVE_INFINITY) { continue; }
double oldOScore = voScore[start][end][cState];
double bestOScore = oldOScore;
UnaryRule[] rules = grammar.getClosedViterbiUnaryRulesByChild(cState);
for (int r = 0; r < rules.length; r++) {
UnaryRule ur = rules[r];
int pState = ur.parentState;
if (cState == pState) continue;
double oS = voScore[start][end][pState];
if (oS == Double.NEGATIVE_INFINITY) { continue; }
double[][] scores = ur.getScores2();
double pS = scores[0][0];
double tot = oS + pS;
if (tot > bestOScore) {
bestOScore = tot;
}
}
if (bestOScore > oldOScore) {
voScore[start][end][cState] = bestOScore;
}
}
for (int pState=0; pState 2) {
int min2 = wideLExtent[end][rState];
min = (min1 > min2 ? min1 : min2);
if (max1 < min) { continue; }
int max2 = wideRExtent[start][lState];
max = (max1 < max2 ? max1 : max2);
if (max < min) { continue; }
}
double[][][] scores = br.getScores2();
double pS = Double.NEGATIVE_INFINITY;//scores[0][0][0];
if (scores[0][0]!=null) pS = scores[0][0][0];
if (pS == Double.NEGATIVE_INFINITY) { continue; }
for (int split = min; split <= max; split++) {
if (!vAllowedStates[start][split]) continue;
if (!vAllowedStates[split][end]) continue;
double lS = viScore[start][split][lState];
if (lS == Double.NEGATIVE_INFINITY) { continue; }
double rS = viScore[split][end][rState];
if (rS == Double.NEGATIVE_INFINITY) { continue; }
double totL = pS + rS + oS;
if (totL > voScore[start][split][lState]) {
voScore[start][split][lState] = totL;
}
double totR = pS + lS + oS;
if (totR > voScore[split][end][rState]) {
voScore[split][end][rState] = totR;
}
}
}
}
}
}
}
// void doConstrainedViterbiSubstateOutsideScores(Grammar grammar) {
// for (int diff = length; diff >= 1; diff--) {
// for (int start = 0; start + diff <= length; start++) {
// int end = start + diff;
// final int lastState = numSubStatesArray.length;
// for (int pState=0; pState bestOScore[cp]) {
// bestOScore[cp] = tot;
// }
// }
// }
// for (int s=0; s oldOScore[s]) {
// oScore[start][end][cState][s] = bestOScore[s];
// }
// }
// }
// }
// for (int pState=0; pState 2) {
// int min2 = wideLExtent[end][rState];
// min = (min1 > min2 ? min1 : min2);
// if (max1 < min) { continue; }
// int max2 = wideRExtent[start][lState];
// max = (max1 < max2 ? max1 : max2);
// if (max < min) { continue; }
// }
//
// double[][][] scores = br.getScores2();
// for (int split = min; split <= max; split++) {
// if (!allowedStates[start][split][lState]) continue;
// if (!allowedStates[split][end][rState]) continue;
//
// for (int lp=0; lp bestLOScore) {
// bestLOScore = totL;
// }
// double totR = pS + lS + oS;
// if (totR > bestROScore) {
// bestROScore = totR;
// }
// }
// if (bestLOScore > oldLOScore) {
// oScore[start][split][lState][lp] = bestLOScore;
// }
// if (bestROScore > oldROScore) {
// oScore[split][end][rState][rp] = bestROScore;
// }
// }
// }
// }
// }
// }
// }
// }
// }
public void printUnaryStats(){
System.out.println("Touched "+touchedRules+" rules.");
System.out.println("Used a total of "+totalUsedUnaries+" unaries.");
System.out.println("Restored "+nTimesRestoredUnaries+" unary chains.");
}
/**
* Return the single best parse.
* Note that the returned tree may be missing intermediate nodes in
* a unary chain because it parses with a unary-closed grammar.
*/
public Tree extractBestViterbiParse(int gState, int gp, int start, int end, List sentence ) {
// find sources of inside score
// no backtraces so we can speed up the parsing for its primary use
double bestScore = iScore[start][end][gState][gp];
String goalStr = (String)tagNumberer.object(gState);
if (goalStr.endsWith("^g")) goalStr = goalStr.substring(0,goalStr.length()-2);
if (outputSub) goalStr = goalStr + "-" + gp;
if (outputScore) goalStr = goalStr + " " + bestScore;
//System.out.println("Looking for "+goalStr+" from "+start+" to "+end+" with score "+ bestScore+".");
if (end - start == 1) {
// if the goal state is a preterminal state, then it can't transform into
// anything but the word below it
if (!grammarTags[gState]) {
List> child = new ArrayList>();
child.add(new Tree(sentence.get(start)));
return new Tree(goalStr, child);
}
// if the goal state is not a preterminal state, then find a way to
// transform it into one
else {
double veryBestScore = Double.NEGATIVE_INFINITY;
int newIndex = -1;
int newCp = -1;
UnaryRule[] unaries = grammar.getClosedViterbiUnaryRulesByParent(gState);
double childScore = bestScore;
for (int r = 0; r < unaries.length; r++) {
UnaryRule ur = unaries[r];
int cState = ur.childState;
if (cState == gState) continue;
if (grammarTags[cState]) continue;
if (!allowedStates[start][end][cState]) continue;
double[][] scores = ur.getScores2();
for (int cp=0; cp= veryBestScore) {
childScore = iScore[start][end][cState][cp];
veryBestScore = ruleScore;
newIndex = cState;
newCp = cp;
}
}
}
List> child1 = new ArrayList>();
child1.add(new Tree(sentence.get(start)));
String goalStr1 = (String) tagNumberer.object(newIndex);
if (outputSub) goalStr1 = goalStr1 + "-" + newCp;
if (outputScore) goalStr1 = goalStr1 + " " + childScore;
if (goalStr1==null)
System.out.println("goalStr1==null with newIndex=="+newIndex+" goalStr=="+goalStr);
List> child = new ArrayList>();
child.add(new Tree(goalStr1, child1));
return new Tree(goalStr, child);
}
}
// check binaries first
BinaryRule[] parentRules = grammar.splitRulesWithP(gState);
for (int split = start + 1; split < end; split++) {
//for (Iterator binaryI = grammar.bRuleIteratorByParent(gState, gp); binaryI.hasNext();) {
//BinaryRule br = (BinaryRule) binaryI.next();
for (int i = 0; i < parentRules.length; i++) {
BinaryRule br = parentRules[i];
int lState = br.leftChildState;
if (iScore[start][split][lState]==null) continue;
int rState = br.rightChildState;
if (iScore[split][end][rState]==null) continue;
//new: iterate over substates
double[][][] scores = br.getScores2();
for (int lp=0; lp leftChildTree = extractBestViterbiParse(lState, lp, start, split, sentence);
Tree rightChildTree = extractBestViterbiParse(rState, rp, split, end, sentence);
List> children = new ArrayList>();
children.add(leftChildTree);
children.add(rightChildTree);
Tree result = new Tree(goalStr, children);
//System.out.println("Binary node: "+result);
//result.setScore(score);
return result;
}
}
}
}
}
// check unaries
//for (Iterator unaryI = grammar.uRuleIteratorByParent(gState, gp); unaryI.hasNext();) {
//UnaryRule ur = (UnaryRule) unaryI.next();
UnaryRule[] unaries = grammar.getClosedViterbiUnaryRulesByParent(gState);
for (int r = 0; r < unaries.length; r++) {
UnaryRule ur = unaries[r];
int cState = ur.childState;
if (cState == gState) continue;
if (iScore[start][end][cState]==null) continue;
//new: iterate over substates
double[][] scores = ur.getScores2();
for (int cp=0; cp childTree = extractBestViterbiParse(cState, cp, start, end, sentence);
List> children = new ArrayList>();
children.add(childTree);
// short intermediateNode = grammar.getUnaryIntermediate((short)gState,(short)cState);
// if (intermediateNode>0){
// List> restoredChild = new ArrayList>();
// nTimesRestoredUnaries++;
// String stateStr2 = (String)tagNumberer.object(intermediateNode);
// if (stateStr2.endsWith("^g")) stateStr2 = stateStr2.substring(0,stateStr2.length()-2);
// if (outputSub) stateStr2 = stateStr2 + "-" + 0;
// if (outputScore) stateStr2 = stateStr2 + " " + childScore;
//
// restoredChild.add(new Tree(stateStr2, children));
// //System.out.println("Restored a unary from "+start+" to "+end+": "+stateStr+" -> "+stateStr2+" -> "+child.get(0).getLabel());
// return new Tree(goalStr,restoredChild);
// }
// else {
Tree result = new Tree(goalStr, children);
return result;
// }
}
}
}
System.err.println("Warning: could not find the optimal way to build state "+goalStr+" spanning from "+ start+ " to "+end+".");
return new Tree("ROOT");
}
public double computeTightThresholds(List sentence) {
clearArrays();
length = (short)sentence.size();
double score = 0;
Grammar curGrammar = null;
Lexicon curLexicon = null;
// double[] pruningThreshold = {-6,-12,-14,-14,-14,-14,-14,-14};//Double.NEGATIVE_INFINITY;//Math.log(1.0e-10);
// double[] pruningThreshold = {-6,-10,-10,-10,-10,-10,-10,-10};//Double.NEGATIVE_INFINITY;//Math.log(1.0e-10);
// double[] pruningThreshold = {-6,-9.75,-10,-9.6,-9.66,-8.01,-7.4,-10};//Double.NEGATIVE_INFINITY;//Math.log(1.0e-10);
double[] pruningThreshold = {-16,-16,-16,-16,-16,-16,-16,-16};
//int startLevel = -1;
for (int level=startLevel; level=0){
minThresh = getTightestThrehold(0,length,0, true, level);
if (minThresh == Double.NEGATIVE_INFINITY) {
System.out.println("Something is wrong.");
return -20;
}
System.out.println("Can set the threshold for level "+level+" to "+minThresh);
maxThresholds[level] = Math.min(maxThresholds[level],minThresh);
}
// pruneChart(minThresh-1, curGrammar.numSubStates, level);
pruneChart(pruningThreshold[level+1], curGrammar.numSubStates, level);
}
return -1.0;
}
private double getTightestThrehold(int start, int end, int state, boolean canStartWithUnary, int level) {
boolean posLevel = (end - start == 1);
if (posLevel) return -2;
double minChildren = Double.POSITIVE_INFINITY;
if (canStartWithUnary){
int cState = maxcChild[start][end][state];
if (cState != -1) {
return getTightestThrehold(start, end, cState, false,level);
}
}
int split = maxcSplit[start][end][state];
double lThresh = getTightestThrehold(start, split, maxcLeftChild[start][end][state], true,level);
double rThresh = getTightestThrehold(split, end, maxcRightChild[start][end][state], true,level);
minChildren = Math.min(lThresh,rThresh);
double sentenceProb = (level<1) ? viScore[0][length][0] : iScore[0][length][0][0];
double maxThreshold = Double.NEGATIVE_INFINITY;
for (int substate=0; substate < grammar.numSubStates[state]; substate++){
double iS = (level<1) ? viScore[start][end][state] : iScore[start][end][state][substate];
double oS = (level<1) ? voScore[start][end][state] : oScore[start][end][state][substate];
if (iS==Double.NEGATIVE_INFINITY||oS==Double.NEGATIVE_INFINITY) continue;
double posterior = iS + oS - sentenceProb;
if (posterior > maxThreshold) maxThreshold = posterior;
}
return Math.min(maxThreshold,minChildren);
}
public void doGoldInsideOutsideScores(Tree tree, List sentence) {
Grammar curGrammar = grammarCascade[endLevel-startLevel+1];
Lexicon curLexicon = lexiconCascade[endLevel-startLevel+1];
//pruneChart(Double.POSITIVE_INFINITY/*pruningThreshold[level+1]*/, curGrammar.numSubStates, endLevel);
allowedStates = new boolean[length][length+1][numStates];
ensureGoldTreeSurvives(tree, endLevel);
double initVal = 0;
int level = isBaseline ? 1 : endLevel;
createArrays(false/*false*/,curGrammar.numStates,curGrammar.numSubStates,level,initVal,false);
//setGoldTreeCountsToOne(tree);
initializeChart(sentence,curLexicon,false,true,null,false);
// doConstrainedInsideScores(curGrammar);
// logLikelihood = Math.log(iScore[0][length][0][0]); // + (100*iScale[0][length][0]);
//
// oScore[0][length][0][0] = 1.0;
// doConstrainedOutsideScores(curGrammar);
}
public Tree removeStars(Tree tree) {
String transformedLabel = tree.getLabel();
int starIndex = transformedLabel.indexOf("*");
if (starIndex != -1) transformedLabel = transformedLabel.substring(0,starIndex);
if (tree.isPreTerminal()) {
return new Tree(transformedLabel,tree.getChildren());
}
List> transformedChildren = new ArrayList>();
for (Tree child : tree.getChildren()) {
transformedChildren.add(removeStars(child));
}
return new Tree(transformedLabel, transformedChildren);
}
private double[] closeVariationalRules(double[][] ruleScores, int start, int end) {
double[] closedScores = new double[numStates];
for (int i = 0; i < numStates; i++) {
closedScores[i] = maxcScore[start][end][i];
}
for (int length=1; length<10; length++){
for (int startState=0; startState closedScores[parentState]){
closedScores[parentState] = newScore;
maxcChild[start][end][parentState] = childState;
}
}
}
return closedScores;
}
void doScaledConstrainedInsideScores(Grammar grammar) {
double initVal = 0;
short[] numSubStatesArray = grammar.numSubStates;
//int smallestScale = 10, largestScale = -10;
for (int diff = 1; diff <= length; diff++) {
//smallestScale = 10; largestScale = -10;
//System.out.print(diff + " ");
for (int start = 0; start < (length - diff + 1); start++) {
int end = start + diff;
for (int pState=0; pState= narrowR); // can this right constituent fit next to the left constituent?
if (!iPossibleR) { continue; }
int min1 = narrowR;
int min2 = wideLExtent[end][rState];
int min = (min1 > min2 ? min1 : min2); // can this right constituent stretch far enough to reach the left constituent?
if (min > narrowL) { continue; }
int max1 = wideRExtent[start][lState];
int max2 = narrowL;
int max = (max1 < max2 ? max1 : max2); // can this left constituent stretch far enough to reach the right constituent?
if (min > max) { continue; }
// TODO switch order of loops for efficiency
double[][][] scores = r.getScores2();
int nLeftChildStates = numSubStatesArray[lState];
int nRightChildStates = numSubStatesArray[rState];
for (int split = min; split <= max; split++) {
boolean changeThisRound = false;
if (allowedStates[start][split][lState] == false) continue;
if (allowedStates[split][end][rState] == false) continue;
for (int lp = 0; lp < nLeftChildStates; lp++) {
double lS = iScore[start][split][lState][lp];
if (lS == initVal) continue;
for (int rp = 0; rp < nRightChildStates; rp++) {
if (scores[lp][rp]==null) continue;
double rS = iScore[split][end][rState][rp];
if (rS == initVal) continue;
for (int np = 0; np < nParentStates; np++) {
if (!allowedSubStates[start][end][pState][np]) continue;
double pS = scores[lp][rp][np];
if (pS == initVal) continue;
double thisRound = pS*lS*rS;
unscaledScoresToAdd[np] += thisRound;
somethingChanged = true;
changeThisRound = true;
}
}
}
if (!changeThisRound) continue;
//boolean firstTime = false;
int parentScale = iScale[start][end][pState];
int currentScale = iScale[start][split][lState]+iScale[split][end][rState];
currentScale = ScalingTools.scaleArray(unscaledScoresToAdd,currentScale);
if (parentScale!=currentScale) {
if (parentScale==Integer.MIN_VALUE){ // first time to build this span
iScale[start][end][pState] = currentScale;
} else {
int newScale = Math.max(currentScale,parentScale);
ScalingTools.scaleArrayToScale(unscaledScoresToAdd,currentScale,newScale);
ScalingTools.scaleArrayToScale(iScore[start][end][pState],parentScale,newScale);
iScale[start][end][pState] = newScale;
}
}
for (int np = 0; np < nParentStates; np++) {
iScore[start][end][pState][np] += unscaledScoresToAdd[np];
}
Arrays.fill(unscaledScoresToAdd,0);
}
}
if (somethingChanged) {
if (start > narrowLExtent[end][pState]) {
narrowLExtent[end][pState] = start;
wideLExtent[end][pState] = start;
} else {
if (start < wideLExtent[end][pState]) {
wideLExtent[end][pState] = start;
}
}
if (end < narrowRExtent[start][pState]) {
narrowRExtent[start][pState] = end;
wideRExtent[start][pState] = end;
} else {
if (end > wideRExtent[start][pState]) {
wideRExtent[start][pState] = end;
}
}
}
}
// now do the unaries
double[][] scoresAfterUnaries = new double[numStates][];
for (int pState=0; pState narrowLExtent[end][pState]) {
narrowLExtent[end][pState] = start;
wideLExtent[end][pState] = start;
} else {
if (start < wideLExtent[end][pState]) {
wideLExtent[end][pState] = start;
}
}
if (end < narrowRExtent[start][pState]) {
narrowRExtent[start][pState] = end;
wideRExtent[start][pState] = end;
} else {
if (end > wideRExtent[start][pState]) {
wideRExtent[start][pState] = end;
}
}
}
// in any case copy/add the scores from before
for (int np = 0; np < nParentStates; np++) {
if (scoresAfterUnaries[pState]==null) continue;
double val = scoresAfterUnaries[pState][np];
if (val>0) {
iScore[start][end][pState][np] += val;
}
}
}
}
}
}
void doScaledConstrainedOutsideScores(Grammar grammar) {
double initVal = 0;
short[] numSubStatesArray = grammar.numSubStates;
// Arrays.fill(scoresToAdd,initVal);
for (int diff = length; diff >= 1; diff--) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
// do unaries
double[][] scoresAfterUnaries = new double[numStates][];
for (int cState=0; cState0) {
oScore[start][end][cState][cp] += val;
}
}
}
// do binaries
if (diff==1) continue; // there is no space for a binary
for (int pState=0; pState 2) {
int min2 = wideLExtent[end][rState];
min = (min1 > min2 ? min1 : min2);
if (max1 < min) { continue; }
int max2 = wideRExtent[start][lState];
max = (max1 < max2 ? max1 : max2);
if (max < min) { continue; }
}
double[][][] scores = br.getScores2();
int nLeftChildStates = numSubStatesArray[lState];
int nRightChildStates = numSubStatesArray[rState];
for (int split = min; split <= max; split++) {
if (allowedStates[start][split][lState] == false) continue;
if (allowedStates[split][end][rState] == false) continue;
boolean somethingChanged = false;
for (int lp=0; lp initVal){
oScore[start][split][lState][cp] += scoresToAdd[cp];
}
}
Arrays.fill(scoresToAdd, 0);
}
if (DoubleArrays.max(unscaledScoresToAdd)!=0){//oScale[start][end][pState]!=Integer.MIN_VALUE && iScale[start][split][lState]!=Integer.MIN_VALUE){
int rightScale = oScale[split][end][rState];
int currentScale = oScale[start][end][pState]+iScale[start][split][lState];
currentScale = ScalingTools.scaleArray(unscaledScoresToAdd,currentScale);
if (rightScale!=currentScale) {
if (rightScale==Integer.MIN_VALUE){ // first time to build this span
oScale[split][end][rState] = currentScale;
} else {
int newScale = Math.max(currentScale,rightScale);
ScalingTools.scaleArrayToScale(unscaledScoresToAdd,currentScale,newScale);
ScalingTools.scaleArrayToScale(oScore[split][end][rState],rightScale,newScale);
oScale[split][end][rState] = newScale;
}
}
for (int cp=0; cp initVal) {
oScore[split][end][rState][cp] += unscaledScoresToAdd[cp];
}
}
Arrays.fill(unscaledScoresToAdd, 0);
}
}
}
}
}
}
}
protected void setupScaling(){
// create arrays for scaling coefficients
iScale = new int[length][length + 1][];
oScale = new int[length][length + 1][];
for (int start = 0; start < length; start++) {
for (int end = start + 1; end <= length; end++) {
iScale[start][end] = new int[numStates];
oScale[start][end] = new int[numStates];
Arrays.fill(iScale[start][end], Integer.MIN_VALUE);
Arrays.fill(oScale[start][end], Integer.MIN_VALUE);
}
}
// scrub the iScores array
for (int start = 0; start < length; start++) {
for (int end = start + 1; end <= length; end++) {
for (int state=0; state parse = getBestParse(nextSentence);
nextSentence = null;
ArrayList> result = new ArrayList>();
result.add(parse);
synchronized(queue) {
queue.add(result,-nextSentenceID);
queue.notifyAll();
}
return null;
}
public CoarseToFineMaxRuleParser newInstance(){
CoarseToFineMaxRuleParser newParser = new CoarseToFineMaxRuleParser(grammar, lexicon, unaryPenalty, endLevel, viterbiParse, outputSub, outputScore, accurate, this.doVariational,useGoldPOS, false);
newParser.initCascade(this);
return newParser;
}
public double getSentenceProbability(int start, int end, boolean sumScores){
// System.out.println((allowedStates[start][end][0]));
// System.out.println((allowedSubStates[start][end][0][0]));
// System.out.println(Arrays.toString(iScore[start][end][0]));
double score = 0;
if (sumScores){
for (int pState=0; pState 0) {
posteriorsToDump = new ArrayList(blockSize);
}
if (posteriorsToDump.size() == blockSize || blockSize == -1) {
fileName = fileName + "." + nThBlock++;
try {
ObjectOutputStream out = new ObjectOutputStream(new GZIPOutputStream(new FileOutputStream(fileName)));
out.writeObject(posteriorsToDump);
out.flush();
out.close();
} catch (IOException e) {
System.out.println("IOException: "+e);
}
if (blockSize==-1) return;
posteriorsToDump = new ArrayList(blockSize);
}
Posterior posterior = new Posterior(iScore, oScore, iScale, oScale, allowedStates);
posteriorsToDump.add(posterior);
}
}
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