public NounPhrase GetNounPhrase(string[] sentence, string[] target)
{
var targetCandidates = GetAllNounPhrases(sentence, target);
TargetCandidate bestCandidate = null;
float bestScore = float.NegativeInfinity;
foreach (var phrase in targetCandidates)
{
var input = new ModelInput
{
Child = phrase.IsChildOfRoot(),
Morphological = (float)phrase.IsMorphologicalSimiliar(lemmatizer),
Sentiment = phrase.IsConnectedToSentiment(),
Wiki = phrase.IsWikipediaTitle(wiki),
Word2Vec = (float)phrase.GetWord2VecCosineSimilarity(word2VecVocabulary),
WordNet = phrase.GetWordNetPathLength(wordNetEngine)
};
ModelOutput result = predictionEngine.Predict(input);
if (result.Score > bestScore)
{
bestCandidate = phrase;
bestScore = result.Score;
}
}
return(bestCandidate.NounPhrase);
}
public object[] GetTrainFeatures(TargetCandidate candidate)
{
return(new object[] {
candidate.IsChildOfRoot(),
candidate.IsWikipediaTitle(wiki),
candidate.IsConnectedToSentiment(),
candidate.IsMorphologicalSimiliar(lemmatizer),
candidate.GetWordNetPathLength(wordNetEngine),
candidate.GetWord2VecCosineSimilarity(word2VecVocabulary)
});
}
