public StatisticalCorefAlgorithm(Properties props, Dictionaries dictionaries, string wordCountsFile, string modelPath, int maxMentionDistance, int maxMentionDistanceWithStringMatch, double[] thresholds)
{
extractor = new FeatureExtractor(props, dictionaries, null, wordCountsFile);
classifier = PairwiseModel.NewBuilder("classifier", MetaFeatureExtractor.NewBuilder().Build()).ModelPath(modelPath).Build();
this.maxMentionDistance = maxMentionDistance;
this.maxMentionDistanceWithStringMatch = maxMentionDistanceWithStringMatch;
this.thresholds = MakeThresholds(thresholds);
}
public ClusteringCorefAlgorithm(Properties props, Dictionaries dictionaries, string clusteringPath, string classificationPath, string rankingPath, string anaphoricityPath, string wordCountsPath)
{
clusterer = new Clusterer(clusteringPath);
classificationModel = PairwiseModel.NewBuilder("classification", MetaFeatureExtractor.NewBuilder().Build()).ModelPath(classificationPath).Build();
rankingModel = PairwiseModel.NewBuilder("ranking", MetaFeatureExtractor.NewBuilder().Build()).ModelPath(rankingPath).Build();
anaphoricityModel = PairwiseModel.NewBuilder("anaphoricity", MetaFeatureExtractor.AnaphoricityMFE()).ModelPath(anaphoricityPath).Build();
extractor = new FeatureExtractor(props, dictionaries, null, wordCountsPath);
}
public FeatureExtractorRunner(Properties props, Dictionaries dictionaries)
{
documents = new List <DocumentExamples>();
compressor = new Compressor <string>();
extractor = new FeatureExtractor(props, dictionaries, compressor);
try
{
dataset = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.datasetFile);
}
catch (Exception e)
{
throw new Exception("Error initializing FeatureExtractorRunner", e);
}
}
