Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* 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 opennlp.tools.parser.chunking;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import opennlp.tools.chunker.Chunker;
import opennlp.tools.chunker.ChunkerME;
import opennlp.tools.chunker.ChunkerModel;
import opennlp.tools.dictionary.Dictionary;
import opennlp.tools.ml.BeamSearch;
import opennlp.tools.ml.EventTrainer;
import opennlp.tools.ml.TrainerFactory;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.MaxentModel;
import opennlp.tools.ml.model.TwoPassDataIndexer;
import opennlp.tools.parser.AbstractBottomUpParser;
import opennlp.tools.parser.ChunkSampleStream;
import opennlp.tools.parser.HeadRules;
import opennlp.tools.parser.Parse;
import opennlp.tools.parser.ParserChunkerFactory;
import opennlp.tools.parser.ParserEventTypeEnum;
import opennlp.tools.parser.ParserModel;
import opennlp.tools.parser.ParserType;
import opennlp.tools.parser.PosSampleStream;
import opennlp.tools.postag.POSModel;
import opennlp.tools.postag.POSTagger;
import opennlp.tools.postag.POSTaggerFactory;
import opennlp.tools.postag.POSTaggerME;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.Span;
import opennlp.tools.util.TrainingParameters;
/**
* Class for a shift reduce style parser based on Adwait Ratnaparkhi's 1998 thesis.
*/
public class Parser extends AbstractBottomUpParser {
private MaxentModel buildModel;
private MaxentModel checkModel;
private BuildContextGenerator buildContextGenerator;
private CheckContextGenerator checkContextGenerator;
private double[] bprobs;
private double[] cprobs;
private static final String TOP_START = START + TOP_NODE;
private int topStartIndex;
private Map startTypeMap;
private Map contTypeMap;
private int completeIndex;
private int incompleteIndex;
public Parser(ParserModel model, int beamSize, double advancePercentage) {
this(model.getBuildModel(), model.getCheckModel(),
new POSTaggerME(model.getParserTaggerModel()),
new ChunkerME(model.getParserChunkerModel()),
model.getHeadRules(), beamSize, advancePercentage);
}
public Parser(ParserModel model) {
this(model, defaultBeamSize, defaultAdvancePercentage);
}
/**
* Creates a new parser using the specified models and head rules using the specified beam
* size and advance percentage.
* @param buildModel The model to assign constituent labels.
* @param checkModel The model to determine a constituent is complete.
* @param tagger The model to assign pos-tags.
* @param chunker The model to assign flat constituent labels.
* @param headRules The head rules for head word perculation.
* @param beamSize The number of different parses kept during parsing.
* @param advancePercentage The minimal amount of probability mass which advanced outcomes must represent.
* Only outcomes which contribute to the top "advancePercentage" will be explored.
*/
private Parser(MaxentModel buildModel, MaxentModel checkModel, POSTagger tagger, Chunker chunker,
HeadRules headRules, int beamSize, double advancePercentage) {
super(tagger, chunker, headRules, beamSize, advancePercentage);
this.buildModel = buildModel;
this.checkModel = checkModel;
bprobs = new double[buildModel.getNumOutcomes()];
cprobs = new double[checkModel.getNumOutcomes()];
this.buildContextGenerator = new BuildContextGenerator();
this.checkContextGenerator = new CheckContextGenerator();
startTypeMap = new HashMap<>();
contTypeMap = new HashMap<>();
for (int boi = 0, bon = buildModel.getNumOutcomes(); boi < bon; boi++) {
String outcome = buildModel.getOutcome(boi);
if (outcome.startsWith(START)) {
//System.err.println("startMap "+outcome+"->"+outcome.substring(START.length()));
startTypeMap.put(outcome, outcome.substring(START.length()));
}
else if (outcome.startsWith(CONT)) {
//System.err.println("contMap "+outcome+"->"+outcome.substring(CONT.length()));
contTypeMap.put(outcome, outcome.substring(CONT.length()));
}
}
topStartIndex = buildModel.getIndex(TOP_START);
completeIndex = checkModel.getIndex(COMPLETE);
incompleteIndex = checkModel.getIndex(INCOMPLETE);
}
@Override
protected void advanceTop(Parse p) {
buildModel.eval(buildContextGenerator.getContext(p.getChildren(), 0), bprobs);
p.addProb(Math.log(bprobs[topStartIndex]));
checkModel.eval(checkContextGenerator.getContext(p.getChildren(), TOP_NODE, 0, 0), cprobs);
p.addProb(Math.log(cprobs[completeIndex]));
p.setType(TOP_NODE);
}
@Override
protected Parse[] advanceParses(final Parse p, double probMass) {
double q = 1 - probMass;
/* The closest previous node which has been labeled as a start node. */
Parse lastStartNode = null;
/* The index of the closest previous node which has been labeled as a start node. */
int lastStartIndex = -1;
/* The type of the closest previous node which has been labeled as a start node. */
String lastStartType = null;
/* The index of the node which will be labeled in this iteration of advancing the parse. */
int advanceNodeIndex;
/* The node which will be labeled in this iteration of advancing the parse. */
Parse advanceNode = null;
Parse[] originalChildren = p.getChildren();
Parse[] children = collapsePunctuation(originalChildren,punctSet);
int numNodes = children.length;
if (numNodes == 0) {
return null;
}
//determines which node needs to be labeled and prior labels.
for (advanceNodeIndex = 0; advanceNodeIndex < numNodes; advanceNodeIndex++) {
advanceNode = children[advanceNodeIndex];
if (advanceNode.getLabel() == null) {
break;
}
else if (startTypeMap.containsKey(advanceNode.getLabel())) {
lastStartType = startTypeMap.get(advanceNode.getLabel());
lastStartNode = advanceNode;
lastStartIndex = advanceNodeIndex;
//System.err.println("lastStart "+i+" "+lastStart.label+" "+lastStart.prob);
}
}
int originalAdvanceIndex = mapParseIndex(advanceNodeIndex,children,originalChildren);
List newParsesList = new ArrayList<>(buildModel.getNumOutcomes());
//call build
buildModel.eval(buildContextGenerator.getContext(children, advanceNodeIndex), bprobs);
double bprobSum = 0;
while (bprobSum < probMass) {
// The largest unadvanced labeling.
int max = 0;
for (int pi = 1; pi < bprobs.length; pi++) { //for each build outcome
if (bprobs[pi] > bprobs[max]) {
max = pi;
}
}
if (bprobs[max] == 0) {
break;
}
double bprob = bprobs[max];
bprobs[max] = 0; //zero out so new max can be found
bprobSum += bprob;
String tag = buildModel.getOutcome(max);
//System.out.println("trying "+tag+" "+bprobSum+" lst="+lst);
if (max == topStartIndex) { // can't have top until complete
continue;
}
//System.err.println(i+" "+tag+" "+bprob);
if (startTypeMap.containsKey(tag)) { //update last start
lastStartIndex = advanceNodeIndex;
lastStartNode = advanceNode;
lastStartType = startTypeMap.get(tag);
}
else if (contTypeMap.containsKey(tag)) {
if (lastStartNode == null || !lastStartType.equals(contTypeMap.get(tag))) {
continue; //Cont must match previous start or continue
}
}
Parse newParse1 = (Parse) p.clone(); //clone parse
if (createDerivationString) newParse1.getDerivation().append(max).append("-");
//replace constituent being labeled to create new derivation
newParse1.setChild(originalAdvanceIndex,tag);
newParse1.addProb(Math.log(bprob));
//check
//String[] context = checkContextGenerator.getContext(newParse1.getChildren(), lastStartType,
// lastStartIndex, advanceNodeIndex);
checkModel.eval(checkContextGenerator.getContext(
collapsePunctuation(newParse1.getChildren(),punctSet), lastStartType, lastStartIndex,
advanceNodeIndex), cprobs);
//System.out.println("check "+lastStartType+" "+cprobs[completeIndex]+" "+cprobs[incompleteIndex]
// +" "+tag+" "+java.util.Arrays.asList(context));
Parse newParse2;
if (cprobs[completeIndex] > q) { //make sure a reduce is likely
newParse2 = (Parse) newParse1.clone();
if (createDerivationString) newParse2.getDerivation().append(1).append(".");
newParse2.addProb(Math.log(cprobs[completeIndex]));
Parse[] cons = new Parse[advanceNodeIndex - lastStartIndex + 1];
boolean flat = true;
//first
cons[0] = lastStartNode;
flat &= cons[0].isPosTag();
//last
cons[advanceNodeIndex - lastStartIndex] = advanceNode;
flat &= cons[advanceNodeIndex - lastStartIndex].isPosTag();
//middle
for (int ci = 1; ci < advanceNodeIndex - lastStartIndex; ci++) {
cons[ci] = children[ci + lastStartIndex];
flat &= cons[ci].isPosTag();
}
if (!flat) { //flat chunks are done by chunker
//check for top node to include end and begining punctuation
if (lastStartIndex == 0 && advanceNodeIndex == numNodes - 1) {
//System.err.println("ParserME.advanceParses: reducing entire span: "
// +new Span(lastStartNode.getSpan().getStart(), advanceNode.getSpan().getEnd())+" "
// +lastStartType+" "+java.util.Arrays.asList(children));
newParse2.insert(new Parse(p.getText(), p.getSpan(), lastStartType, cprobs[1],
headRules.getHead(cons, lastStartType)));
}
else {
newParse2.insert(new Parse(p.getText(), new Span(lastStartNode.getSpan().getStart(),
advanceNode.getSpan().getEnd()), lastStartType, cprobs[1],
headRules.getHead(cons, lastStartType)));
}
newParsesList.add(newParse2);
}
}
if (cprobs[incompleteIndex] > q) { //make sure a shift is likely
if (createDerivationString) newParse1.getDerivation().append(0).append(".");
if (advanceNodeIndex != numNodes - 1) { //can't shift last element
newParse1.addProb(Math.log(cprobs[incompleteIndex]));
newParsesList.add(newParse1);
}
}
}
Parse[] newParses = new Parse[newParsesList.size()];
newParsesList.toArray(newParses);
return newParses;
}
/**
* @deprecated Please do not use anymore, use the ObjectStream train methods instead! This method
* will be removed soon.
*/
@Deprecated
public static AbstractModel train(ObjectStream es, int iterations, int cut) throws IOException {
return opennlp.tools.ml.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
}
public static void mergeReportIntoManifest(Map manifest,
Map report, String namespace) {
for (Map.Entry entry : report.entrySet()) {
manifest.put(namespace + "." + entry.getKey(), entry.getValue());
}
}
public static ParserModel train(String languageCode, ObjectStream parseSamples,
HeadRules rules, TrainingParameters mlParams)
throws IOException {
System.err.println("Building dictionary");
Dictionary mdict = buildDictionary(parseSamples, rules, mlParams);
parseSamples.reset();
Map manifestInfoEntries = new HashMap<>();
// build
System.err.println("Training builder");
ObjectStream bes = new ParserEventStream(parseSamples, rules, ParserEventTypeEnum.BUILD, mdict);
Map buildReportMap = new HashMap<>();
EventTrainer buildTrainer =
TrainerFactory.getEventTrainer(mlParams.getSettings("build"), buildReportMap);
MaxentModel buildModel = buildTrainer.train(bes);
mergeReportIntoManifest(manifestInfoEntries, buildReportMap, "build");
parseSamples.reset();
// tag
TrainingParameters posTaggerParams = mlParams.getParameters("tagger");
if (!posTaggerParams.getSettings().containsKey(BeamSearch.BEAM_SIZE_PARAMETER)) {
mlParams.put("tagger", BeamSearch.BEAM_SIZE_PARAMETER,
Integer.toString(10));
}
POSModel posModel = POSTaggerME.train(languageCode, new PosSampleStream(parseSamples),
mlParams.getParameters("tagger"), new POSTaggerFactory());
parseSamples.reset();
// chunk
ChunkerModel chunkModel = ChunkerME.train(languageCode,
new ChunkSampleStream(parseSamples), mlParams.getParameters("chunker"), new ParserChunkerFactory());
parseSamples.reset();
// check
System.err.println("Training checker");
ObjectStream kes = new ParserEventStream(parseSamples, rules, ParserEventTypeEnum.CHECK);
Map checkReportMap = new HashMap<>();
EventTrainer checkTrainer =
TrainerFactory.getEventTrainer(mlParams.getSettings("check"), checkReportMap);
MaxentModel checkModel = checkTrainer.train(kes);
mergeReportIntoManifest(manifestInfoEntries, checkReportMap, "check");
// TODO: Remove cast for HeadRules
return new ParserModel(languageCode, buildModel, checkModel,
posModel, chunkModel, rules,
ParserType.CHUNKING, manifestInfoEntries);
}
}
