public Set<Feature> extract(JCas jcas) {
double nrOfNPs = 0.0;
double nrOfVPs = 0.0;
double nrOfPPs = 0.0;
int nrOfSbars = 0;
int nrOfVerbphrases = 0;
int nrOfComplexNominals = 0;
double nrOfClauses = 0.0;
int nrOfDependentClauses = 0;
double nrOfTunits = 0.0;
int nrOfComplexTunits = 0;
int nrOfCoords = 0;
int lengthSumNPs = 0;
int lengthSumVPs = 0;
int lengthSumPPs = 0;
int lengthSumClauses = 0;
int lengthSumTunits = 0;
int parseTreeDepthSum = 0;
Set<Feature> featSet = new HashSet<Feature>();
double nrOfSentences = JCasUtil.select(jcas, Sentence.class).size() * 1.0;
for (Sentence s : JCasUtil.select(jcas, Sentence.class)) {
parseTreeDepthSum += ParsePatternUtils.getParseDepth(s);
for (Constituent c : JCasUtil.selectCovered(Constituent.class, s)) {
if (c instanceof NP) {
nrOfNPs++;
lengthSumNPs += c.getCoveredText().length();
} else if (c instanceof VP) {
nrOfVPs++;
lengthSumVPs += c.getCoveredText().length();
} else if (c instanceof PP) {
nrOfPPs++;
lengthSumPPs += c.getCoveredText().length();
} else if (c instanceof SBAR) {
nrOfSbars++;
if (ParsePatternUtils.isDependentClause(c)) {
nrOfDependentClauses++;
}
} else if (ParsePatternUtils.isClause(c)) {
nrOfClauses++;
lengthSumClauses += c.getCoveredText().length();
}
if (ParsePatternUtils.isTunit(c)) {
nrOfTunits++;
lengthSumTunits += c.getCoveredText().length();
if (ParsePatternUtils.isComplexTunit(c)) {
nrOfComplexTunits++;
}
}
if (ParsePatternUtils.isCoordinate(c)) {
nrOfCoords++;
}
if (ParsePatternUtils.isComplexNominal(c)) {
nrOfComplexNominals++;
}
if (ParsePatternUtils.isVerbPhrase(c)) {
nrOfVerbphrases++;
}
}
}
// avoid division by zero, there should be at least one sentence in the cas
nrOfSentences = Math.max(1, nrOfSentences);
featSet.addAll(Arrays.asList(new Feature(NPS_PER_SENTENCE, nrOfNPs / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(VPS_PER_SENTENCE, nrOfVPs / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(PPS_PER_SENTENCE, nrOfPPs / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(SBARS_PER_SENTENCE, nrOfSbars / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(CLAUSES_PER_SENTENCE, nrOfClauses / nrOfSentences)));
featSet.addAll(
Arrays.asList(new Feature(DEP_CLAUSES_PER_SENTENCE, nrOfDependentClauses / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(TUNITS_PER_SENTENCE, nrOfTunits / nrOfSentences)));
featSet.addAll(
Arrays.asList(new Feature(COMPLEX_TUNITS_PER_SENTENCE, nrOfComplexTunits / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(COORDS_PER_SENTENCE, nrOfCoords / nrOfSentences)));
// avoid division by 0,
// if we don't have any NPs, the lengthSum is 0, division by 1 will yield 0 as average
// length
nrOfNPs = Math.max(1, nrOfNPs);
nrOfVPs = Math.max(1, nrOfVPs);
nrOfPPs = Math.max(1, nrOfPPs);
nrOfTunits = Math.max(1, nrOfTunits);
featSet.addAll(Arrays.asList(new Feature(AVG_NP_LENGTH, lengthSumNPs / nrOfNPs)));
featSet.addAll(Arrays.asList(new Feature(AVG_VP_LENGTH, lengthSumVPs / nrOfVPs)));
featSet.addAll(Arrays.asList(new Feature(AVG_PP_LENGTH, lengthSumPPs / nrOfPPs)));
featSet.addAll(Arrays.asList(new Feature(AVG_TUNIT_LENGTH, lengthSumTunits / nrOfTunits)));
featSet.addAll(Arrays.asList(new Feature(AVG_TREE_DEPTH, parseTreeDepthSum / nrOfSentences)));
featSet.addAll(Arrays.asList(new Feature(CLAUSES_PER_TUNIT, nrOfClauses / nrOfTunits)));
nrOfClauses = Math.max(1, nrOfClauses);
featSet.addAll(Arrays.asList(new Feature(AVG_CLAUSE_LENGTH, lengthSumClauses / nrOfClauses)));
featSet.addAll(
Arrays.asList(new Feature(COMPLEX_TUNITS_PER_TUNIT, nrOfComplexTunits / nrOfTunits)));
featSet.addAll(Arrays.asList(new Feature(COORDS_PER_TUNIT, nrOfCoords / nrOfTunits)));
featSet.addAll(
Arrays.asList(new Feature(COMPLEXNOMINALS_PER_TUNIT, nrOfComplexNominals / nrOfTunits)));
featSet.addAll(Arrays.asList(new Feature(VERBPHRASES_PER_TUNIT, nrOfVerbphrases / nrOfTunits)));
featSet.addAll(
Arrays.asList(new Feature(DEPCLAUSE_TUNIT_RATIO, nrOfDependentClauses / nrOfTunits)));
;
featSet.addAll(
Arrays.asList(new Feature(DEPCLAUSE_CLAUSE_RATIO, nrOfDependentClauses / nrOfClauses)));
featSet.addAll(Arrays.asList(new Feature(COORDS_PER_CLAUSE, nrOfCoords / nrOfClauses)));
;
featSet.addAll(
Arrays.asList(new Feature(COMPLEXNOMINALS_PER_CLAUSE, nrOfComplexNominals / nrOfClauses)));
;
return featSet;
}
