// from TestKea
private void setOptions(String model) {
// Name of the model -- give the path to the model
ke.setModelName(model);
// Name of the vocabulary -- name of the file (without extension) that is stored in
// VOCABULARIES
// or "none" if no Vocabulary is used (free keyphrase extraction).
// ke.setVocabulary("agrovoc");
ke.setVocabulary("none");
// Optional arguments if you want to change the defaults
// Encoding of the document
ke.setEncoding("UTF-8");
// Language of the document -- use "es" for Spanish, "fr" for French
// or other languages as specified in your "skos" vocabulary
ke.setDocumentLanguage("en"); // es for Spanish, fr for French
// Stemmer -- adjust if you use a different language than English or want to alterate results
// (We have obtained better results for Spanish and French with NoStemmer)
ke.setStemmer(new PorterStemmer());
// Stopwords
ke.setStopwords(new StopwordsEnglish());
// Number of Keyphrases to extract
ke.setNumPhrases(5);
// Set to true, if you want to compute global dictionaries from the test collection
ke.setBuildGlobal(false);
}
private List<Instance> myExtractKeyphrases(String document, int numOfPhrases) throws Exception {
// Check whether there is actually any data
//
if (document.length() == 0 || document.equals("")) {
throw new Exception("Couldn't find any data!");
}
FastVector atts = new FastVector(3);
atts.addElement(new Attribute("doc", (FastVector) null));
atts.addElement(new Attribute("keyphrases", (FastVector) null));
Instances data = new Instances("keyphrase_training_data", atts, 0);
List<Instance> myInstances = new ArrayList<Instance>();
double[] newInst = new double[2];
newInst[0] = (double) data.attribute(0).addStringValue(document);
newInst[1] = Instance.missingValue();
data.add(new Instance(1.0, newInst));
m_KEAFilter.input(data.instance(0));
data = data.stringFreeStructure();
ke.setNumPhrases(numOfPhrases);
int numPhrases = numOfPhrases; // ke.getNumPhrases();
Instance[] topRankedInstances = new Instance[numPhrases];
Instance inst;
// Iterating over all extracted keyphrases (inst)
while ((inst = m_KEAFilter.output()) != null) {
int index = (int) inst.value(m_KEAFilter.getRankIndex()) - 1;
if (index < numPhrases) {
topRankedInstances[index] = inst;
}
}
double numExtracted = 0, numCorrect = 0;
for (int i = 0; i < numPhrases; i++) {
if (topRankedInstances[i] != null) {
if (!topRankedInstances[i].isMissing(topRankedInstances[i].numAttributes() - 1)) {
numExtracted += 1.0;
}
if ((int) topRankedInstances[i].value(topRankedInstances[i].numAttributes() - 1) == 1) {
numCorrect += 1.0;
}
myInstances.add(topRankedInstances[i]);
}
}
return myInstances;
}