public RelationExtractorAnnotator(Properties props)
{
verbose = bool.Parse(props.GetProperty("sup.relation.verbose", "false"));
string relationModel = props.GetProperty("sup.relation.model", DefaultPaths.DefaultSupRelationExRelationModel);
try
{
IExtractor entityExtractor = new RothEntityExtractor();
BasicRelationExtractor relationExtractor = BasicRelationExtractor.Load(relationModel);
log.Info("Loading relation model from " + relationModel);
mr = MachineReading.MakeMachineReadingForAnnotation(new RothCONLL04Reader(), entityExtractor, relationExtractor, null, null, null, true, verbose);
}
catch (Exception e)
{
Sharpen.Runtime.PrintStackTrace(e);
throw new Exception(e);
}
}
/*
* FeatureFactory class to use for generating features from relations for relation extraction.
* Default is "edu.stanford.nlp.ie.machinereading.RelationFeatureFactory"
*/
//private Class<RelationFeatureFactory> relationFeatureFactoryClass = edu.stanford.nlp.ie.machinereading.RelationFeatureFactory.class;
/*
* comma-separated list of feature types to generate for relation extraction
*/
//private String relationFeatures;
public override void PrintResults(PrintWriter pw, IList <ICoreMap> goldStandard, IList <ICoreMap> extractorOutput)
{
featureFactory = MachineReading.MakeRelationFeatureFactory(MachineReadingProperties.relationFeatureFactoryClass, MachineReadingProperties.relationFeatures, false);
mentionFactory = new RelationMentionFactory();
// generic mentions work well in this domain
ResultsPrinter.Align(goldStandard, extractorOutput);
IList <RelationMention> relations = new List <RelationMention>();
IDictionary <RelationMention, string> predictions = new Dictionary <RelationMention, string>();
for (int i = 0; i < goldStandard.Count; i++)
{
IList <RelationMention> goldRelations = AnnotationUtils.GetAllRelations(mentionFactory, goldStandard[i], true);
Sharpen.Collections.AddAll(relations, goldRelations);
foreach (RelationMention rel in goldRelations)
{
predictions[rel] = AnnotationUtils.GetRelation(mentionFactory, extractorOutput[i], rel.GetArg(0), rel.GetArg(1)).GetType();
}
}
ICounter <Pair <Pair <string, string>, string> > pathCounts = new ClassicCounter <Pair <Pair <string, string>, string> >();
foreach (RelationMention rel_1 in relations)
{
pathCounts.IncrementCount(new Pair <Pair <string, string>, string>(new Pair <string, string>(rel_1.GetArg(0).GetType(), rel_1.GetArg(1).GetType()), featureFactory.GetFeature(rel_1, "dependency_path_lowlevel")));
}
ICounter <string> singletonCorrect = new ClassicCounter <string>();
ICounter <string> singletonPredicted = new ClassicCounter <string>();
ICounter <string> singletonActual = new ClassicCounter <string>();
foreach (RelationMention rel_2 in relations)
{
if (pathCounts.GetCount(new Pair <Pair <string, string>, string>(new Pair <string, string>(rel_2.GetArg(0).GetType(), rel_2.GetArg(1).GetType()), featureFactory.GetFeature(rel_2, "dependency_path_lowlevel"))) == 1.0)
{
string prediction = predictions[rel_2];
if (prediction.Equals(rel_2.GetType()))
{
singletonCorrect.IncrementCount(prediction);
}
singletonPredicted.IncrementCount(prediction);
singletonActual.IncrementCount(rel_2.GetType());
}
}
// Group together actual relations of a type with relations that were
// predicted to be that type
// String rel1group = RelationsSentence.isUnrelatedLabel(rel1.getType())
// ? prediction1 : rel1.getType();
// String rel2group = RelationsSentence.isUnrelatedLabel(rel2.getType())
// ? prediction2 : rel2.getType();
// int groupComp = rel1group.compareTo(rel2group);
// int pathComp =
// getFeature(rel1,"generalized_dependency_path").compareTo(getFeature(rel2,"generalized_dependency_path"));
// } else if (pathComp != 0) {
// return pathComp;
_T1018869951 relComp = new _T1018869951(this);
relations.Sort(relComp);
foreach (RelationMention rel_3 in relations)
{
string prediction = predictions[rel_3];
// if (RelationsSentence.isUnrelatedLabel(prediction) &&
// RelationsSentence.isUnrelatedLabel(rel.getType())) {
// continue;
// }
string type1 = rel_3.GetArg(0).GetType();
string type2 = rel_3.GetArg(1).GetType();
string path = featureFactory.GetFeature(rel_3, "dependency_path_lowlevel");
if (!((type1.Equals("PEOPLE") && type2.Equals("PEOPLE")) || (type1.Equals("PEOPLE") && type2.Equals("LOCATION")) || (type1.Equals("LOCATION") && type2.Equals("LOCATION")) || (type1.Equals("ORGANIZATION") && type2.Equals("LOCATION")) || (type1
.Equals("PEOPLE") && type2.Equals("ORGANIZATION"))))
{
continue;
}
if (path.Equals(string.Empty))
{
continue;
}
pw.Println("\nLABEL: " + prediction);
pw.Println(rel_3);
pw.Println(path);
pw.Println(featureFactory.GetFeatures(rel_3, "dependency_path_words"));
pw.Println(featureFactory.GetFeature(rel_3, "surface_path_POS"));
}
}
/// <summary>Loads the model from disk.</summary>
/// <param name="path">The location of model that was saved to disk</param>
/// <exception cref="System.InvalidCastException">if model is the wrong format</exception>
/// <exception cref="System.IO.IOException">
/// if the model file doesn't exist or is otherwise
/// unavailable/incomplete
/// </exception>
/// <exception cref="System.TypeLoadException">this would probably indicate a serious classpath problem</exception>
public static Edu.Stanford.Nlp.IE.Machinereading.BasicEntityExtractor Load(string path, Type entityClassifier, bool preferDefaultGazetteer)
{
// load the additional arguments
// try to load the extra file from the CLASSPATH first
InputStream @is = typeof(Edu.Stanford.Nlp.IE.Machinereading.BasicEntityExtractor).GetClassLoader().GetResourceAsStream(path + ".extra");
// if not found in the CLASSPATH, load from the file system
if (@is == null)
{
@is = new FileInputStream(path + ".extra");
}
ObjectInputStream @in = new ObjectInputStream(@is);
string gazetteerLocation = ErasureUtils.UncheckedCast <string>(@in.ReadObject());
if (preferDefaultGazetteer)
{
gazetteerLocation = DefaultPaths.DefaultNflGazetteer;
}
ICollection <string> annotationsToSkip = ErasureUtils.UncheckedCast <ICollection <string> >(@in.ReadObject());
bool useSubTypes = ErasureUtils.UncheckedCast <bool>(@in.ReadObject());
bool useBIO = ErasureUtils.UncheckedCast <bool>(@in.ReadObject());
@in.Close();
@is.Close();
Edu.Stanford.Nlp.IE.Machinereading.BasicEntityExtractor extractor = (Edu.Stanford.Nlp.IE.Machinereading.BasicEntityExtractor)MachineReading.MakeEntityExtractor(entityClassifier, gazetteerLocation);
// load the CRF classifier (this works from any resource, e.g., classpath or file system)
extractor.classifier = CRFClassifier.GetClassifier(path);
// copy the extra arguments
extractor.annotationsToSkip = annotationsToSkip;
extractor.useSubTypes = useSubTypes;
extractor.useBIO = useBIO;
return(extractor);
}
