/**
* train the tagger using the DocumentCollection in file 'trainingCollection'.
* 'trainingCollection' should consist of documents which have been explicitly tagged with
* part-of-speech information.
*/
void train(String trainingCollection) {
for (int i = 0; i < posTable.length; i++)
tagTable[i] = new String[] {"constit", "cat", posTable[i], posTable[i]};
// build ergodic HMM with one state for each POS (plus start and end states)
HMMstate startState = new HMMstate("start", "", WordFeatureHMMemitter.class);
posh.addState(startState);
for (int j = 0; j < posTable.length; j++) startState.addArc(new HMMarc(posTable[j], 0));
HMMstate endState = new HMMstate("end", "", WordFeatureHMMemitter.class);
posh.addState(endState);
for (int i = 0; i < posTable.length; i++) {
String pos = posTable[i];
HMMstate state = new HMMstate(pos, pos, WordFeatureHMMemitter.class);
posh.addState(state);
for (int j = 0; j < posTable.length; j++) state.addArc(new HMMarc(posTable[j], 0));
state.addArc(new HMMarc("end", 0));
}
posh.resolveNames();
posh.resetForTraining();
annotator = new HMMannotator(posh);
annotator.setTagTable(tagTable);
annotator.setBItag(false);
DocumentCollection col = new DocumentCollection(trainingCollection);
col.open();
for (int i = 0; i < col.size(); i++) {
ExternalDocument doc = col.get(i);
doc.open();
System.out.println("Training from " + doc.fileName());
// divide at endmarks (constit cat="."), adding "S" marks
int posn = 0;
int start = posn;
Vector anns;
while ((anns = doc.annotationsAt(posn, "constit")) != null) {
Annotation ann = (Annotation) anns.get(0);
posn = ann.span().end();
String pos = (String) ann.get("cat");
if (pos.equals(".")) {
doc.annotate("S", new Span(start, posn), new FeatureSet());
start = posn;
}
}
annotator.train(doc);
// free up space taken by annotations on document
doc.clearAnnotations();
}
posh.computeProbabilities();
}
public void run() {
List<TestFailure> failures = dto.loadFailures();
DocumentCollection documents = DocumentCollection.getInstance();
TermCollection terms = TermCollection.getInstance();
TestFailureUtil.buildDocumentCollection(failures, documents);
TestFailureUtil.buildTermCollection(failures, terms);
for (int i = 0; i < terms.size(); i++) {
String term = terms.get(i);
Integer count = documents.findTermFrequency(term);
// Calculate idf weight against a term
Float weight = (float) Math.log((double) documents.size() / (double) count);
terms.setWeithg(term, weight);
// System.out.println(String.valueOf(i) + "." + term + " : " + String.valueOf(weight)) ;
}
for (int i = 0; i < documents.size(); i++) {
String document = documents.get(i);
String[] words = document.split("\\s+");
System.out.println(document);
for (String word : words) {
TermItem item = new TermItem();
float tfidf = VectorSpaceModel.findTFIDF(documents.get(i), word);
item.setTerm(word);
item.setTfidf(tfidf);
Integer pos = -1;
while ((pos = document.indexOf(word, pos + 1)) >= 0) {
item.addPos(pos);
}
if (tfidf > 0.0) System.out.println(item.toString());
}
}
}
