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A Java UIMA-based toolbox for multilingual and efficient terminology extraction an multilingual term alignment
/*******************************************************************************
* Copyright 2015 - CNRS (Centre National de Recherche Scientifique)
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*
*******************************************************************************/
package eu.project.ttc.models;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.apache.commons.lang3.mutable.MutableInt;
import com.google.common.base.Joiner;
import com.google.common.base.Objects;
import com.google.common.base.Preconditions;
import com.google.common.collect.ComparisonChain;
import com.google.common.collect.Iterables;
import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import eu.project.ttc.metrics.AssociationRate;
/**
*
* A sorted Term/Frequency map used as a context vector during alignment operations.
*
* @author Damien Cram
*
*/
public class ContextVector {
private static final String MSG_TERM_CLASS_NULL = "Flag useTermClasses is set to true but term classes are not built.";
private Map entries = Maps.newHashMap();
private List _sortedEntries = null;
private int totalCooccurrences = 0;
private boolean useTermClasses = false;
private Term term;
/**
* Default constructor for {@link ContextVector}. Must be used if no normalization is required.
*/
public ContextVector() {
}
/**
*
* Construct a context vector with a back reference to its owner term.
* The parent term is useful for normalization.
*
* @see ContextVector#toAssocRateVector(CrossTable, AssociationRate, boolean)
* @param term
* the owner term
* @param useTermClasses
* if true, builds this context vector with term
* classes instead of terms
*/
public ContextVector(Term term, boolean useTermClasses) {
this(term);
this.useTermClasses = useTermClasses;
}
/**
*
* Construct a context vector with a back reference to its owner term.
* The parent term is useful for normalization.
*
* @see ContextVector#toAssocRateVector(CrossTable, AssociationRate, boolean)
* @param term
* the owner term
*/
public ContextVector(Term term) {
this.term = term;
}
public void addAllCooccurrences(Iterator it) {
while(it.hasNext()) {
addCooccurrence(it.next());
}
}
public void addCooccurrence(TermOccurrence occ) {
Term term2 = getTermToAdd(occ.getTerm());
if(!entries.containsKey(term2))
entries.put(term2, new Entry(term2));
entries.get(term2).increment();
this.totalCooccurrences++;
setDirty();
}
private Term getTermToAdd(Term t) {
Term termToAdd = t;
if(this.useTermClasses) {
Preconditions.checkState(termToAdd.getTermClass() != null, MSG_TERM_CLASS_NULL);
termToAdd = termToAdd.getTermClass().getHead();
}
return termToAdd;
}
public void addEntry(Term coTerm, int nbCooccs, double assocRate) {
Term termToAdd = getTermToAdd(coTerm);
if(entries.containsKey(termToAdd))
this.totalCooccurrences -= entries.get(termToAdd).getNbCooccs();
entries.put(termToAdd, new Entry(termToAdd, nbCooccs, assocRate));
this.totalCooccurrences+=nbCooccs;
setDirty();
}
public void removeCoTerm(Term term) {
Term termToAdd = getTermToAdd(term);
if(this.entries.containsKey(termToAdd)) {
Entry e = this.entries.remove(termToAdd);
this.totalCooccurrences -= e.getNbCooccs();
}
setDirty();
}
private void setDirty() {
this._sortedEntries = null;
}
/**
* Gives the list of terms in this context vector sorted by frequency
* (most frequent first)
*
* @return
*/
public List getEntries() {
if(_sortedEntries == null) {
this._sortedEntries = Lists.newArrayListWithCapacity(entries.size());
for(Map.Entry e:entries.entrySet())
this._sortedEntries.add(e.getValue());
Collections.sort(this._sortedEntries);
}
return _sortedEntries;
}
/**
* Normalizes all assocRates so that their sum is 1
*/
public void normalize() {
double sum = 0;
for(Entry e:entries.values())
sum+=e.getAssocRate();
if(sum != 0) {
for(Entry e:entries.values())
e.setAssocRate(e.getAssocRate()/sum);
}
}
public class Entry implements Comparable {
private static final String STRING_FORMAT = "%s: %.3f (%d)";
private Term coTerm;
private MutableInt nbCooccs;
private double assocRate;
private Entry(Term term) {
this(term, 0, 0d);
}
private Entry(Term coTerm, int nbCoccurrences, double assocRate) {
this.coTerm = coTerm;
this.nbCooccs = new MutableInt(nbCoccurrences);
this.assocRate = assocRate;
}
private void increment() {
this.nbCooccs.increment();
}
public void setAssocRate(double assocRate) {
this.assocRate = assocRate;
}
public double getAssocRate() {
return assocRate;
}
public Term getCoTerm() {
return coTerm;
}
public int getNbCooccs() {
return nbCooccs.intValue();
}
@Override
public boolean equals(Object obj) {
if (obj instanceof Entry)
return Objects.equal(((Entry) obj).coTerm, this.coTerm)
&& Objects.equal(((Entry) obj).nbCooccs, this.nbCooccs)
&& Objects.equal(((Entry) obj).assocRate, this.assocRate);
else
return false;
}
@Override
public int hashCode() {
return this.coTerm.getId();
}
@Override
public int compareTo(Entry o) {
return ComparisonChain.start()
.compare(o.assocRate, this.assocRate)
.compare(o.nbCooccs, this.nbCooccs)
.result();
}
@Override
public String toString() {
return String.format(STRING_FORMAT, coTerm.getLemma(), assocRate, nbCooccs.intValue());
}
}
public int getNbCooccs(Term term) {
return entries.containsKey(term) ? entries.get(term).getNbCooccs() : 0;
}
public double getAssocRate(Term term) {
return entries.containsKey(term) ? entries.get(term).getAssocRate() : 0d;
}
public Set terms() {
return entries.keySet();
}
@Override
public int hashCode() {
return Objects.hashCode(this.term);
}
@Override
public boolean equals(Object obj) {
if (obj instanceof ContextVector) {
ContextVector v = (ContextVector) obj;
return Objects.equal(this.term, v.term) && Iterables.elementsEqual(this.getEntries(), v.getEntries());
} else
return false;
}
public Term getTerm() {
return term;
}
/**
* Normalize this vector according to a cross table
* and an association rate measure.
*
* This method recomputes all {@link Entry}.frequency values
* with the normalized ones.
*
* @param table
* the pre-computed co-occurrences {@link CrossTable}
* @param assocRateFunction
* the {@link AssociationRate} measure implementation
* @param normalize
*/
public void toAssocRateVector(CrossTable table, AssociationRate assocRateFunction, boolean normalize) {
double assocRate;
for(Term coterm:entries.keySet()) {
assocRate = table.computeRate(assocRateFunction, this.term, coterm);
entries.get(coterm).setAssocRate(assocRate);
}
if(normalize)
normalize();
setDirty();
}
private final static String STRING_FORMAT = "<%s> {%s}";
@Override
public String toString() {
return String.format(STRING_FORMAT, this.term == null ? "no parent term" : this.term.getLemma(), Joiner.on(", ").join(getEntries()));
}
public int getTotalCoccurrences() {
return this.totalCooccurrences;
}
}
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