public void process(JCas jCas) throws AnalysisEngineProcessException {
TriageScore doc = JCasUtil.selectSingle(jCas, TriageScore.class);
String code = doc.getInOutCode();
File outFile = new File(baseData.getPath() + "/" + code + ".txt");
try {
PrintWriter out = new PrintWriter(new BufferedWriter(new FileWriter(outFile, true)));
out.print(doc.getVpdmfId() + " ");
for (Sentence sentence : JCasUtil.select(jCas, Sentence.class)) {
List<Token> tokens = JCasUtil.selectCovered(jCas, Token.class, sentence);
if (tokens.size() <= 0) {
continue;
}
List<String> tokenStrings = JCasUtil.toText(tokens);
for (int i = 0; i < tokens.size(); i++) {
out.print(tokenStrings.get(i) + " ");
}
}
out.print("\n");
out.close();
} catch (IOException e) {
throw new AnalysisEngineProcessException(e);
}
}
@Override
public ClassificationTEDecision process(JCas jcas) throws EDAException, ComponentException {
Pair pair = JCasUtil.selectSingle(jcas, Pair.class);
// Compute similarity scores with all components
List<Double> scores = new ArrayList<Double>();
for (ScoringComponent component : getComponents()) {
Vector<Double> subscores = component.calculateScores(jcas);
scores.addAll(subscores);
}
// If multiple components have been used, we use the highest score
// to determine the Entailment/NonEntailment relationship.
// This is intended for illustration purposes only, as the similarity
// scores are not normally distributed.
double maxScore = Collections.max(scores);
DecisionLabel label;
if (maxScore >= threshold) label = DecisionLabel.Entailment;
else label = DecisionLabel.NonEntailment;
return new ClassificationTEDecision(label, scores.get(0), pair.getPairID());
}
