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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.
//
// StanfordCoreNLP -- a suite of NLP tools
// Copyright (c) 2009-2010 The Board of Trustees of
// The Leland Stanford Junior University. All Rights Reserved.
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
//
// For more information, bug reports, fixes, contact:
// Christopher Manning
// Dept of Computer Science, Gates 1A
// Stanford CA 94305-9010
// USA
//
package edu.stanford.nlp.coref.data;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collections;
import java.util.EnumSet;
import java.util.List;
import java.util.Set;
import java.util.TreeMap;
import edu.stanford.nlp.coref.data.Dictionaries.Animacy;
import edu.stanford.nlp.coref.data.Dictionaries.Gender;
import edu.stanford.nlp.coref.data.Dictionaries.Number;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.util.Generics;
import edu.stanford.nlp.util.logging.Redwood;
/**
* One cluster for the SieveCoreferenceSystem.
*
* @author Heeyoung Lee
*/
public class CorefCluster implements Serializable{
private static final long serialVersionUID = 8655265337578515592L;
public final Set corefMentions;
public final int clusterID;
// Attributes for cluster - can include multiple attribute e.g., {singular, plural}
public final Set numbers;
public final Set genders;
public final Set animacies;
public final Set nerStrings;
public final Set heads;
/** All words in this cluster - for word inclusion feature */
public final Set words;
/** The first mention in this cluster */
protected Mention firstMention;
/** Return the most representative mention in the chain.
* A proper noun mention or a mention with more pre-modifiers is preferred.
*/
public Mention representative;
public int getClusterID(){ return clusterID; }
public Set getCorefMentions() { return corefMentions; }
public int size() { return corefMentions.size(); }
public Mention getFirstMention() { return firstMention; }
public Mention getRepresentativeMention() { return representative; }
public CorefCluster(int ID) {
clusterID = ID;
corefMentions = Generics.newHashSet();
numbers = EnumSet.noneOf(Number.class);
genders = EnumSet.noneOf(Gender.class);
animacies = EnumSet.noneOf(Animacy.class);
nerStrings = Generics.newHashSet();
heads = Generics.newHashSet();
words = Generics.newHashSet();
firstMention = null;
representative = null;
}
public CorefCluster(int ID, Set mentions){
this(ID);
// Register mentions
corefMentions.addAll(mentions);
// Get list of mentions in textual order
List sortedMentions = new ArrayList<>(mentions.size());
sortedMentions.addAll(mentions);
Collections.sort(sortedMentions, new CorefChain.MentionComparator());
// Set default for first / representative mention
if (sortedMentions.size() > 0) {
firstMention = sortedMentions.get(0);
representative = sortedMentions.get(0); // will be updated below
}
for (Mention m : sortedMentions) {
// Add various information about mentions to cluster
animacies.add(m.animacy);
genders.add(m.gender);
numbers.add(m.number);
nerStrings.add(m.nerString);
heads.add(m.headString);
if(!m.isPronominal()){
for(CoreLabel w : m.originalSpan){
words.add(w.get(CoreAnnotations.TextAnnotation.class).toLowerCase());
}
}
// Update representative mention, if appropriate
if (m != representative && m.moreRepresentativeThan(representative)) {
assert !representative.moreRepresentativeThan(m);
representative = m;
}
}
}
/** merge 2 clusters: to = to + from */
public static void mergeClusters(CorefCluster to, CorefCluster from) {
int toID = to.clusterID;
for (Mention m : from.corefMentions){
m.corefClusterID = toID;
}
to.numbers.addAll(from.numbers);
if(to.numbers.size() > 1 && to.numbers.contains(Number.UNKNOWN)) {
to.numbers.remove(Number.UNKNOWN);
}
to.genders.addAll(from.genders);
if(to.genders.size() > 1 && to.genders.contains(Gender.UNKNOWN)) {
to.genders.remove(Gender.UNKNOWN);
}
to.animacies.addAll(from.animacies);
if(to.animacies.size() > 1 && to.animacies.contains(Animacy.UNKNOWN)) {
to.animacies.remove(Animacy.UNKNOWN);
}
to.nerStrings.addAll(from.nerStrings);
if(to.nerStrings.size() > 1 && to.nerStrings.contains("O")) {
to.nerStrings.remove("O");
}
if(to.nerStrings.size() > 1 && to.nerStrings.contains("MISC")) {
to.nerStrings.remove("MISC");
}
to.heads.addAll(from.heads);
to.corefMentions.addAll(from.corefMentions);
to.words.addAll(from.words);
if(from.firstMention.appearEarlierThan(to.firstMention) && !from.firstMention.isPronominal()) {
assert !to.firstMention.appearEarlierThan(from.firstMention);
to.firstMention = from.firstMention;
}
if(from.representative.moreRepresentativeThan(to.representative)) to.representative = from.representative;
//Redwood.log("debug-cluster", "merged clusters: "+toID+" += "+from.clusterID);
//to.printCorefCluster();
//from.printCorefCluster();
}
/** Print cluster information */
public void printCorefCluster(){
Redwood.log("debug-cluster", "Cluster ID: "+clusterID+"\tNumbers: "+numbers+"\tGenders: "+genders+"\tanimacies: "+animacies);
Redwood.log("debug-cluster", "NE: "+nerStrings+"\tfirst Mention's ID: "+firstMention.mentionID+"\tHeads: "+heads+"\twords: "+words);
TreeMap forSortedPrint = new TreeMap<>();
for(Mention m : this.corefMentions){
forSortedPrint.put(m.mentionID, m);
}
for(Mention m : forSortedPrint.values()){
String rep = (representative == m)? "*":"";
if(m.goldCorefClusterID==-1){
Redwood.log("debug-cluster", rep + "mention-> id:"+m.mentionID+"\toriginalRef: "
+m.originalRef+"\t"+m.spanToString() +"\tsentNum: "+m.sentNum+"\tstartIndex: "
+m.startIndex+"\tType: "+m.mentionType+"\tNER: "+m.nerString);
} else{
Redwood.log("debug-cluster", rep + "mention-> id:"+m.mentionID+"\toriginalClusterID: "
+m.goldCorefClusterID+"\t"+m.spanToString() +"\tsentNum: "+m.sentNum+"\tstartIndex: "
+m.startIndex +"\toriginalRef: "+m.originalRef+"\tType: "+m.mentionType+"\tNER: "+m.nerString);
}
}
}
public boolean isSinglePronounCluster(Dictionaries dict){
if(this.corefMentions.size() > 1) return false;
for(Mention m : this.corefMentions) {
if(m.isPronominal() || dict.allPronouns.contains(m.spanToString().toLowerCase())) return true;
}
return false;
}
@Override
public String toString(){
return corefMentions.toString()+"="+clusterID;
}
}