public static void main(String[] args) {
OptionParser optParser = new OptionParser(Options.class);
Options opts = (Options) optParser.parse(args, true);
// provide feedback on command-line arguments
System.out.println("Calling with " + optParser.getPassedInOptions());
String path = opts.path;
// int lang = opts.lang;
System.out.println("Loading trees from " + path + " and using language " + opts.treebank);
double trainingFractionToKeep = opts.trainingFractionToKeep;
int maxSentenceLength = opts.maxSentenceLength;
System.out.println("Will remove sentences with more than " + maxSentenceLength + " words.");
HORIZONTAL_MARKOVIZATION = opts.horizontalMarkovization;
VERTICAL_MARKOVIZATION = opts.verticalMarkovization;
System.out.println(
"Using horizontal="
+ HORIZONTAL_MARKOVIZATION
+ " and vertical="
+ VERTICAL_MARKOVIZATION
+ " markovization.");
Binarization binarization = opts.binarization;
System.out.println(
"Using " + binarization.name() + " binarization."); // and "+annotateString+".");
double randomness = opts.randomization;
System.out.println("Using a randomness value of " + randomness);
String outFileName = opts.outFileName;
if (outFileName == null) {
System.out.println("Output File name is required.");
System.exit(-1);
} else System.out.println("Using grammar output file " + outFileName + ".");
VERBOSE = opts.verbose;
RANDOM = new Random(opts.randSeed);
System.out.println("Random number generator seeded at " + opts.randSeed + ".");
boolean manualAnnotation = false;
boolean baseline = opts.baseline;
boolean noSplit = opts.noSplit;
int numSplitTimes = opts.numSplits;
if (baseline) numSplitTimes = 0;
String splitGrammarFile = opts.inFile;
int allowedDroppingIters = opts.di;
int maxIterations = opts.splitMaxIterations;
int minIterations = opts.splitMinIterations;
if (minIterations > 0)
System.out.println("I will do at least " + minIterations + " iterations.");
double[] smoothParams = {opts.smoothingParameter1, opts.smoothingParameter2};
System.out.println("Using smoothing parameters " + smoothParams[0] + " and " + smoothParams[1]);
boolean allowMoreSubstatesThanCounts = false;
boolean findClosedUnaryPaths = opts.findClosedUnaryPaths;
Corpus corpus =
new Corpus(
path,
opts.treebank,
trainingFractionToKeep,
false,
opts.skipSection,
opts.skipBilingual);
List<Tree<String>> trainTrees =
Corpus.binarizeAndFilterTrees(
corpus.getTrainTrees(),
VERTICAL_MARKOVIZATION,
HORIZONTAL_MARKOVIZATION,
maxSentenceLength,
binarization,
manualAnnotation,
VERBOSE);
List<Tree<String>> validationTrees =
Corpus.binarizeAndFilterTrees(
corpus.getValidationTrees(),
VERTICAL_MARKOVIZATION,
HORIZONTAL_MARKOVIZATION,
maxSentenceLength,
binarization,
manualAnnotation,
VERBOSE);
Numberer tagNumberer = Numberer.getGlobalNumberer("tags");
// for (Tree<String> t : trainTrees){
// System.out.println(t);
// }
if (opts.trainOnDevSet) {
System.out.println("Adding devSet to training data.");
trainTrees.addAll(validationTrees);
}
if (opts.lowercase) {
System.out.println("Lowercasing the treebank.");
Corpus.lowercaseWords(trainTrees);
Corpus.lowercaseWords(validationTrees);
}
int nTrees = trainTrees.size();
System.out.println("There are " + nTrees + " trees in the training set.");
double filter = opts.filter;
if (filter > 0)
System.out.println(
"Will remove rules with prob under "
+ filter
+ ".\nEven though only unlikely rules are pruned the training LL is not guaranteed to increase in every round anymore "
+ "(especially when we are close to converging)."
+ "\nFurthermore it increases the variance because 'good' rules can be pruned away in early stages.");
short nSubstates = opts.nSubStates;
short[] numSubStatesArray =
initializeSubStateArray(trainTrees, validationTrees, tagNumberer, nSubstates);
if (baseline) {
short one = 1;
Arrays.fill(numSubStatesArray, one);
System.out.println("Training just the baseline grammar (1 substate for all states)");
randomness = 0.0f;
}
if (VERBOSE) {
for (int i = 0; i < numSubStatesArray.length; i++) {
System.out.println("Tag " + (String) tagNumberer.object(i) + " " + i);
}
}
System.out.println("There are " + numSubStatesArray.length + " observed categories.");
// initialize lexicon and grammar
Lexicon lexicon = null, maxLexicon = null, previousLexicon = null;
Grammar grammar = null, maxGrammar = null, previousGrammar = null;
double maxLikelihood = Double.NEGATIVE_INFINITY;
// String smootherStr = opts.smooth;
// Smoother lexiconSmoother = null;
// Smoother grammarSmoother = null;
// if (splitGrammarFile!=null){
// lexiconSmoother = maxLexicon.smoother;
// grammarSmoother = maxGrammar.smoother;
// System.out.println("Using smoother from input grammar.");
// }
// else if (smootherStr.equals("NoSmoothing"))
// lexiconSmoother = grammarSmoother = new NoSmoothing();
// else if (smootherStr.equals("SmoothAcrossParentBits")) {
// lexiconSmoother = grammarSmoother = new SmoothAcrossParentBits(grammarSmoothing,
// maxGrammar.splitTrees);
// }
// else
// throw new Error("I didn't understand the type of smoother '"+smootherStr+"'");
// System.out.println("Using smoother "+smootherStr);
// EM: iterate until the validation likelihood drops for four consecutive
// iterations
int iter = 0;
int droppingIter = 0;
// If we are splitting, we load the old grammar and start off by splitting.
int startSplit = 0;
if (splitGrammarFile != null) {
System.out.println("Loading old grammar from " + splitGrammarFile);
startSplit = 1; // we've already trained the grammar
ParserData pData = ParserData.Load(splitGrammarFile);
maxGrammar = pData.gr;
maxLexicon = pData.lex;
numSubStatesArray = maxGrammar.numSubStates;
previousGrammar = grammar = maxGrammar;
previousLexicon = lexicon = maxLexicon;
Numberer.setNumberers(pData.getNumbs());
tagNumberer = Numberer.getGlobalNumberer("tags");
System.out.println("Loading old grammar complete.");
if (noSplit) {
System.out.println("Will NOT split the loaded grammar.");
startSplit = 0;
}
}
double mergingPercentage = opts.mergingPercentage;
boolean separateMergingThreshold = opts.separateMergingThreshold;
if (mergingPercentage > 0) {
System.out.println(
"Will merge " + (int) (mergingPercentage * 100) + "% of the splits in each round.");
System.out.println(
"The threshold for merging lexical and phrasal categories will be set separately: "
+ separateMergingThreshold);
}
StateSetTreeList trainStateSetTrees =
new StateSetTreeList(trainTrees, numSubStatesArray, false, tagNumberer);
StateSetTreeList validationStateSetTrees =
new StateSetTreeList(validationTrees, numSubStatesArray, false, tagNumberer); // deletePC);
// get rid of the old trees
trainTrees = null;
validationTrees = null;
corpus = null;
System.gc();
if (opts.simpleLexicon) {
System.out.println(
"Replacing words which have been seen less than 5 times with their signature.");
Corpus.replaceRareWords(
trainStateSetTrees, new SimpleLexicon(numSubStatesArray, -1), opts.rare);
}
// If we're training without loading a split grammar, then we run once without splitting.
if (splitGrammarFile == null) {
grammar =
new Grammar(numSubStatesArray, findClosedUnaryPaths, new NoSmoothing(), null, filter);
Lexicon tmp_lexicon =
(opts.simpleLexicon)
? new SimpleLexicon(
numSubStatesArray,
-1,
smoothParams,
new NoSmoothing(),
filter,
trainStateSetTrees)
: new SophisticatedLexicon(
numSubStatesArray,
SophisticatedLexicon.DEFAULT_SMOOTHING_CUTOFF,
smoothParams,
new NoSmoothing(),
filter);
int n = 0;
boolean secondHalf = false;
for (Tree<StateSet> stateSetTree : trainStateSetTrees) {
secondHalf = (n++ > nTrees / 2.0);
tmp_lexicon.trainTree(stateSetTree, randomness, null, secondHalf, false, opts.rare);
}
lexicon =
(opts.simpleLexicon)
? new SimpleLexicon(
numSubStatesArray,
-1,
smoothParams,
new NoSmoothing(),
filter,
trainStateSetTrees)
: new SophisticatedLexicon(
numSubStatesArray,
SophisticatedLexicon.DEFAULT_SMOOTHING_CUTOFF,
smoothParams,
new NoSmoothing(),
filter);
for (Tree<StateSet> stateSetTree : trainStateSetTrees) {
secondHalf = (n++ > nTrees / 2.0);
lexicon.trainTree(stateSetTree, randomness, tmp_lexicon, secondHalf, false, opts.rare);
grammar.tallyUninitializedStateSetTree(stateSetTree);
}
lexicon.tieRareWordStats(opts.rare);
lexicon.optimize();
// SSIE
((SophisticatedLexicon) lexicon).overwriteWithMaxent();
grammar.optimize(randomness);
// System.out.println(grammar);
previousGrammar = maxGrammar = grammar; // needed for baseline - when there is no EM loop
previousLexicon = maxLexicon = lexicon;
}
// the main loop: split and train the grammar
for (int splitIndex = startSplit; splitIndex < numSplitTimes * 3; splitIndex++) {
// now do either a merge or a split and the end a smooth
// on odd iterations merge, on even iterations split
String opString = "";
if (splitIndex % 3 == 2) { // (splitIndex==numSplitTimes*2){
if (opts.smooth.equals("NoSmoothing")) continue;
System.out.println("Setting smoother for grammar and lexicon.");
Smoother grSmoother = new SmoothAcrossParentBits(0.01, maxGrammar.splitTrees);
Smoother lexSmoother = new SmoothAcrossParentBits(0.1, maxGrammar.splitTrees);
// Smoother grSmoother = new SmoothAcrossParentSubstate(0.01);
// Smoother lexSmoother = new SmoothAcrossParentSubstate(0.1);
maxGrammar.setSmoother(grSmoother);
maxLexicon.setSmoother(lexSmoother);
minIterations = maxIterations = opts.smoothMaxIterations;
opString = "smoothing";
} else if (splitIndex % 3 == 0) {
// the case where we split
if (opts.noSplit) continue;
System.out.println(
"Before splitting, we have a total of " + maxGrammar.totalSubStates() + " substates.");
CorpusStatistics corpusStatistics = new CorpusStatistics(tagNumberer, trainStateSetTrees);
int[] counts = corpusStatistics.getSymbolCounts();
maxGrammar = maxGrammar.splitAllStates(randomness, counts, allowMoreSubstatesThanCounts, 0);
maxLexicon = maxLexicon.splitAllStates(counts, allowMoreSubstatesThanCounts, 0);
Smoother grSmoother = new NoSmoothing();
Smoother lexSmoother = new NoSmoothing();
maxGrammar.setSmoother(grSmoother);
maxLexicon.setSmoother(lexSmoother);
System.out.println(
"After splitting, we have a total of " + maxGrammar.totalSubStates() + " substates.");
System.out.println(
"Rule probabilities are NOT normalized in the split, therefore the training LL is not guaranteed to improve between iteration 0 and 1!");
opString = "splitting";
maxIterations = opts.splitMaxIterations;
minIterations = opts.splitMinIterations;
} else {
if (mergingPercentage == 0) continue;
// the case where we merge
double[][] mergeWeights =
GrammarMerger.computeMergeWeights(maxGrammar, maxLexicon, trainStateSetTrees);
double[][][] deltas =
GrammarMerger.computeDeltas(maxGrammar, maxLexicon, mergeWeights, trainStateSetTrees);
boolean[][][] mergeThesePairs =
GrammarMerger.determineMergePairs(
deltas, separateMergingThreshold, mergingPercentage, maxGrammar);
grammar = GrammarMerger.doTheMerges(maxGrammar, maxLexicon, mergeThesePairs, mergeWeights);
short[] newNumSubStatesArray = grammar.numSubStates;
trainStateSetTrees = new StateSetTreeList(trainStateSetTrees, newNumSubStatesArray, false);
validationStateSetTrees =
new StateSetTreeList(validationStateSetTrees, newNumSubStatesArray, false);
// retrain lexicon to finish the lexicon merge (updates the unknown words model)...
lexicon =
(opts.simpleLexicon)
? new SimpleLexicon(
newNumSubStatesArray,
-1,
smoothParams,
maxLexicon.getSmoother(),
filter,
trainStateSetTrees)
: new SophisticatedLexicon(
newNumSubStatesArray,
SophisticatedLexicon.DEFAULT_SMOOTHING_CUTOFF,
maxLexicon.getSmoothingParams(),
maxLexicon.getSmoother(),
maxLexicon.getPruningThreshold());
boolean updateOnlyLexicon = true;
double trainingLikelihood =
GrammarTrainer.doOneEStep(
grammar,
maxLexicon,
null,
lexicon,
trainStateSetTrees,
updateOnlyLexicon,
opts.rare);
// System.out.println("The training LL is "+trainingLikelihood);
lexicon
.optimize(); // Grammar.RandomInitializationType.INITIALIZE_WITH_SMALL_RANDOMIZATION);
// // M Step
GrammarMerger.printMergingStatistics(maxGrammar, grammar);
opString = "merging";
maxGrammar = grammar;
maxLexicon = lexicon;
maxIterations = opts.mergeMaxIterations;
minIterations = opts.mergeMinIterations;
}
// update the substate dependent objects
previousGrammar = grammar = maxGrammar;
previousLexicon = lexicon = maxLexicon;
droppingIter = 0;
numSubStatesArray = grammar.numSubStates;
trainStateSetTrees = new StateSetTreeList(trainStateSetTrees, numSubStatesArray, false);
validationStateSetTrees =
new StateSetTreeList(validationStateSetTrees, numSubStatesArray, false);
maxLikelihood = calculateLogLikelihood(maxGrammar, maxLexicon, validationStateSetTrees);
System.out.println(
"After "
+ opString
+ " in the "
+ (splitIndex / 3 + 1)
+ "th round, we get a validation likelihood of "
+ maxLikelihood);
iter = 0;
// the inner loop: train the grammar via EM until validation likelihood reliably drops
do {
iter += 1;
System.out.println("Beginning iteration " + (iter - 1) + ":");
// 1) Compute the validation likelihood of the previous iteration
System.out.print("Calculating validation likelihood...");
double validationLikelihood =
calculateLogLikelihood(
previousGrammar,
previousLexicon,
validationStateSetTrees); // The validation LL of previousGrammar/previousLexicon
System.out.println("done: " + validationLikelihood);
// 2) Perform the E step while computing the training likelihood of the previous iteration
System.out.print("Calculating training likelihood...");
grammar =
new Grammar(
grammar.numSubStates,
grammar.findClosedPaths,
grammar.smoother,
grammar,
grammar.threshold);
lexicon =
(opts.simpleLexicon)
? new SimpleLexicon(
grammar.numSubStates,
-1,
smoothParams,
lexicon.getSmoother(),
filter,
trainStateSetTrees)
: new SophisticatedLexicon(
grammar.numSubStates,
SophisticatedLexicon.DEFAULT_SMOOTHING_CUTOFF,
lexicon.getSmoothingParams(),
lexicon.getSmoother(),
lexicon.getPruningThreshold());
boolean updateOnlyLexicon = false;
double trainingLikelihood =
doOneEStep(
previousGrammar,
previousLexicon,
grammar,
lexicon,
trainStateSetTrees,
updateOnlyLexicon,
opts.rare); // The training LL of previousGrammar/previousLexicon
System.out.println("done: " + trainingLikelihood);
// 3) Perform the M-Step
lexicon.optimize(); // M Step
grammar.optimize(0); // M Step
// 4) Check whether previousGrammar/previousLexicon was in fact better than the best
if (iter < minIterations || validationLikelihood >= maxLikelihood) {
maxLikelihood = validationLikelihood;
maxGrammar = previousGrammar;
maxLexicon = previousLexicon;
droppingIter = 0;
} else {
droppingIter++;
}
// 5) advance the 'pointers'
previousGrammar = grammar;
previousLexicon = lexicon;
} while ((droppingIter < allowedDroppingIters) && (!baseline) && (iter < maxIterations));
// Dump a grammar file to disk from time to time
ParserData pData =
new ParserData(
maxLexicon,
maxGrammar,
null,
Numberer.getNumberers(),
numSubStatesArray,
VERTICAL_MARKOVIZATION,
HORIZONTAL_MARKOVIZATION,
binarization);
String outTmpName = outFileName + "_" + (splitIndex / 3 + 1) + "_" + opString + ".gr";
System.out.println("Saving grammar to " + outTmpName + ".");
if (pData.Save(outTmpName)) System.out.println("Saving successful.");
else System.out.println("Saving failed!");
pData = null;
}
// The last grammar/lexicon has not yet been evaluated. Even though the validation likelihood
// has been dropping in the past few iteration, there is still a chance that the last one was in
// fact the best so just in case we evaluate it.
System.out.print("Calculating last validation likelihood...");
double validationLikelihood = calculateLogLikelihood(grammar, lexicon, validationStateSetTrees);
System.out.println(
"done.\n Iteration "
+ iter
+ " (final) gives validation likelihood "
+ validationLikelihood);
if (validationLikelihood > maxLikelihood) {
maxLikelihood = validationLikelihood;
maxGrammar = previousGrammar;
maxLexicon = previousLexicon;
}
ParserData pData =
new ParserData(
maxLexicon,
maxGrammar,
null,
Numberer.getNumberers(),
numSubStatesArray,
VERTICAL_MARKOVIZATION,
HORIZONTAL_MARKOVIZATION,
binarization);
System.out.println("Saving grammar to " + outFileName + ".");
System.out.println("It gives a validation data log likelihood of: " + maxLikelihood);
if (pData.Save(outFileName)) System.out.println("Saving successful.");
else System.out.println("Saving failed!");
System.exit(0);
}
/**
* Run from the command-line, with a list of URLs as argument.
*
* <p><B>NOTE:</B><br>
* This code will run with all the documents in memory - if you want to unload each from memory
* after use, add code to store the corpus in a DataStore.
*/
public static void main(String args[]) throws GateException, IOException {
// initialise the GATE library
Out.prln("Initialising GATE...");
Gate.init();
Out.prln("...GATE initialised");
// initialise ANNIE (this may take several minutes)
StandAloneAnnie annie = new StandAloneAnnie();
annie.initAnnie();
// create a GATE corpus and add a document for each command-line
// argument
Corpus corpus = Factory.newCorpus("StandAloneAnnie corpus");
for (int i = 0; i < args.length; i++) {
URL u = new URL(args[i]);
FeatureMap params = Factory.newFeatureMap();
params.put("sourceUrl", u);
params.put("preserveOriginalContent", new Boolean(true));
params.put("collectRepositioningInfo", new Boolean(true));
Out.prln("Creating doc for " + u);
Document doc = (Document) Factory.createResource("gate.corpora.DocumentImpl", params);
corpus.add(doc);
} // for each of args
// tell the pipeline about the corpus and run it
annie.setCorpus(corpus);
annie.execute();
// for each document, get an XML document with the
// person and location names added
Iterator iter = corpus.iterator();
int count = 0;
String startTagPart_1 = "<span GateID=\"";
String startTagPart_2 = "\" title=\"";
String startTagPart_3 = "\" style=\"background:Red;\">";
String endTag = "</span>";
while (iter.hasNext()) {
Document doc = (Document) iter.next();
AnnotationSet defaultAnnotSet = doc.getAnnotations();
Set annotTypesRequired = new HashSet();
annotTypesRequired.add("Person");
annotTypesRequired.add("Location");
Set<Annotation> peopleAndPlaces =
new HashSet<Annotation>(defaultAnnotSet.get(annotTypesRequired));
FeatureMap features = doc.getFeatures();
String originalContent =
(String) features.get(GateConstants.ORIGINAL_DOCUMENT_CONTENT_FEATURE_NAME);
RepositioningInfo info =
(RepositioningInfo) features.get(GateConstants.DOCUMENT_REPOSITIONING_INFO_FEATURE_NAME);
++count;
File file = new File("StANNIE_" + count + ".HTML");
Out.prln("File name: '" + file.getAbsolutePath() + "'");
if (originalContent != null && info != null) {
Out.prln("OrigContent and reposInfo existing. Generate file...");
Iterator it = peopleAndPlaces.iterator();
Annotation currAnnot;
SortedAnnotationList sortedAnnotations = new SortedAnnotationList();
while (it.hasNext()) {
currAnnot = (Annotation) it.next();
sortedAnnotations.addSortedExclusive(currAnnot);
} // while
StringBuffer editableContent = new StringBuffer(originalContent);
long insertPositionEnd;
long insertPositionStart;
// insert anotation tags backward
Out.prln("Unsorted annotations count: " + peopleAndPlaces.size());
Out.prln("Sorted annotations count: " + sortedAnnotations.size());
for (int i = sortedAnnotations.size() - 1; i >= 0; --i) {
currAnnot = (Annotation) sortedAnnotations.get(i);
insertPositionStart = currAnnot.getStartNode().getOffset().longValue();
insertPositionStart = info.getOriginalPos(insertPositionStart);
insertPositionEnd = currAnnot.getEndNode().getOffset().longValue();
insertPositionEnd = info.getOriginalPos(insertPositionEnd, true);
if (insertPositionEnd != -1 && insertPositionStart != -1) {
editableContent.insert((int) insertPositionEnd, endTag);
editableContent.insert((int) insertPositionStart, startTagPart_3);
editableContent.insert((int) insertPositionStart, currAnnot.getType());
editableContent.insert((int) insertPositionStart, startTagPart_2);
editableContent.insert((int) insertPositionStart, currAnnot.getId().toString());
editableContent.insert((int) insertPositionStart, startTagPart_1);
} // if
} // for
FileWriter writer = new FileWriter(file);
writer.write(editableContent.toString());
writer.close();
} // if - should generate
else if (originalContent != null) {
Out.prln("OrigContent existing. Generate file...");
Iterator it = peopleAndPlaces.iterator();
Annotation currAnnot;
SortedAnnotationList sortedAnnotations = new SortedAnnotationList();
while (it.hasNext()) {
currAnnot = (Annotation) it.next();
sortedAnnotations.addSortedExclusive(currAnnot);
} // while
StringBuffer editableContent = new StringBuffer(originalContent);
long insertPositionEnd;
long insertPositionStart;
// insert anotation tags backward
Out.prln("Unsorted annotations count: " + peopleAndPlaces.size());
Out.prln("Sorted annotations count: " + sortedAnnotations.size());
for (int i = sortedAnnotations.size() - 1; i >= 0; --i) {
currAnnot = (Annotation) sortedAnnotations.get(i);
insertPositionStart = currAnnot.getStartNode().getOffset().longValue();
insertPositionEnd = currAnnot.getEndNode().getOffset().longValue();
if (insertPositionEnd != -1 && insertPositionStart != -1) {
editableContent.insert((int) insertPositionEnd, endTag);
editableContent.insert((int) insertPositionStart, startTagPart_3);
editableContent.insert((int) insertPositionStart, currAnnot.getType());
editableContent.insert((int) insertPositionStart, startTagPart_2);
editableContent.insert((int) insertPositionStart, currAnnot.getId().toString());
editableContent.insert((int) insertPositionStart, startTagPart_1);
} // if
} // for
FileWriter writer = new FileWriter(file);
writer.write(editableContent.toString());
writer.close();
} else {
Out.prln("Content : " + originalContent);
Out.prln("Repositioning: " + info);
}
String xmlDocument = doc.toXml(peopleAndPlaces, false);
String fileName = new String("StANNIE_toXML_" + count + ".HTML");
FileWriter writer = new FileWriter(fileName);
writer.write(xmlDocument);
writer.close();
} // for each doc
} // main