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 agreemnets. 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.treeinsert;
import java.io.IOException;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;
import opennlp.model.AbstractModel;
import opennlp.model.MaxentModel;
import opennlp.model.TwoPassDataIndexer;
import opennlp.tools.chunker.Chunker;
import opennlp.tools.chunker.ChunkerME;
import opennlp.tools.chunker.ChunkerModel;
import opennlp.tools.dictionary.Dictionary;
import opennlp.tools.parser.AbstractBottomUpParser;
import opennlp.tools.parser.ChunkContextGenerator;
import opennlp.tools.parser.ChunkSampleStream;
import opennlp.tools.parser.HeadRules;
import opennlp.tools.parser.Parse;
import opennlp.tools.parser.ParseSampleStream;
import opennlp.tools.parser.ParserChunkerSequenceValidator;
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.POSTaggerME;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.PlainTextByLineStream;
import opennlp.tools.util.model.ModelType;
/**
* Built/attach parser. Nodes are built when their left-most
* child is encountered. Subsequent children are attached as
* daughters. Attachment is based on node in the right-frontier
* of the tree. After each attachment or building, nodes are
* assesed as either complete or incomplete. Complete nodes
* are no longer elligable for daughter attachment.
* Complex modifiers which produce additional node
* levels of the same type are attached with sister-adjunction.
* Attachment can not take place higher in the right-frontier
* than an incomplete node.
*/
public class Parser extends AbstractBottomUpParser {
/** Outcome used when a constituent needs an no additional parent node/building. */
public static final String DONE = "d";
/** Outcome used when a node should be attached as a sister to another node. */
public static final String ATTACH_SISTER = "s";
/** Outcome used when a node should be attached as a daughter to another node. */
public static final String ATTACH_DAUGHTER = "d";
/** Outcome used when a node should not be attached to another node. */
public static final String NON_ATTACH = "n";
/** Label used to distinguish build nodes from non-built nodes. */
public static final String BUILT = "built";
private MaxentModel buildModel;
private MaxentModel attachModel;
private MaxentModel checkModel;
static boolean checkComplete = false;
private BuildContextGenerator buildContextGenerator;
private AttachContextGenerator attachContextGenerator;
private CheckContextGenerator checkContextGenerator;
private double[] bprobs;
private double[] aprobs;
private double[] cprobs;
private int doneIndex;
private int sisterAttachIndex;
private int daughterAttachIndex;
private int nonAttachIndex;
private int completeIndex;
private int[] attachments;
public Parser(ParserModel model, int beamSize, double advancePercentage) {
this(model.getBuildModel(), model.getAttachModel(), model.getCheckModel(),
new POSTaggerME(model.getParserTaggerModel()),
new ChunkerME(model.getParserChunkerModel(),
ChunkerME.DEFAULT_BEAM_SIZE,
new ParserChunkerSequenceValidator(model.getParserChunkerModel()),
new ChunkContextGenerator(ChunkerME.DEFAULT_BEAM_SIZE)),
model.getHeadRules(),
beamSize, advancePercentage);
}
public Parser(ParserModel model) {
this(model, defaultBeamSize, defaultAdvancePercentage);
}
@Deprecated
public Parser(AbstractModel buildModel, AbstractModel attachModel, AbstractModel checkModel, POSTagger tagger, Chunker chunker, HeadRules headRules, int beamSize, double advancePercentage) {
super(tagger,chunker,headRules,beamSize,advancePercentage);
this.buildModel = buildModel;
this.attachModel = attachModel;
this.checkModel = checkModel;
this.buildContextGenerator = new BuildContextGenerator();
this.attachContextGenerator = new AttachContextGenerator(punctSet);
this.checkContextGenerator = new CheckContextGenerator(punctSet);
this.bprobs = new double[buildModel.getNumOutcomes()];
this.aprobs = new double[attachModel.getNumOutcomes()];
this.cprobs = new double[checkModel.getNumOutcomes()];
this.doneIndex = buildModel.getIndex(DONE);
this.sisterAttachIndex = attachModel.getIndex(ATTACH_SISTER);
this.daughterAttachIndex = attachModel.getIndex(ATTACH_DAUGHTER);
this.nonAttachIndex = attachModel.getIndex(NON_ATTACH);
attachments = new int[] {daughterAttachIndex,sisterAttachIndex};
this.completeIndex = checkModel.getIndex(Parser.COMPLETE);
}
@Deprecated
public Parser(AbstractModel buildModel, AbstractModel attachModel, AbstractModel checkModel, POSTagger tagger, Chunker chunker, HeadRules headRules) {
this(buildModel,attachModel,checkModel, tagger,chunker,headRules,defaultBeamSize,defaultAdvancePercentage);
}
/**
* Returns the right frontier of the specified parse tree with nodes ordered from deepest
* to shallowest.
* @param root The root of the parse tree.
* @return The right frontier of the specified parse tree.
*/
public static List getRightFrontier(Parse root,Set punctSet) {
List rf = new LinkedList();
Parse top;
if (root.getType() == AbstractBottomUpParser.TOP_NODE ||
root.getType() == AbstractBottomUpParser.INC_NODE) {
top = collapsePunctuation(root.getChildren(),punctSet)[0];
}
else {
top = root;
}
while(!top.isPosTag()) {
rf.add(0,top);
Parse[] kids = top.getChildren();
top = kids[kids.length-1];
}
return new ArrayList(rf);
}
private void setBuilt(Parse p) {
String l = p.getLabel();
if (l == null) {
p.setLabel(Parser.BUILT);
}
else {
if (isComplete(p)) {
p.setLabel(Parser.BUILT+"."+Parser.COMPLETE);
}
else {
p.setLabel(Parser.BUILT+"."+Parser.INCOMPLETE);
}
}
}
private void setComplete(Parse p) {
String l = p.getLabel();
if (!isBuilt(p)) {
p.setLabel(Parser.COMPLETE);
}
else {
p.setLabel(Parser.BUILT+"."+Parser.COMPLETE);
}
}
private void setIncomplete(Parse p) {
if (!isBuilt(p)) {
p.setLabel(Parser.INCOMPLETE);
}
else {
p.setLabel(Parser.BUILT+"."+Parser.INCOMPLETE);
}
}
private boolean isBuilt(Parse p) {
String l = p.getLabel();
if (l == null) {
return false;
}
else {
return l.startsWith(Parser.BUILT);
}
}
private boolean isComplete(Parse p) {
String l = p.getLabel();
if (l == null) {
return false;
}
else {
return l.endsWith(Parser.COMPLETE);
}
}
protected Parse[] advanceChunks(Parse p, double minChunkScore) {
Parse[] parses = super.advanceChunks(p, minChunkScore);
for (int pi=0;pi newParsesList = new ArrayList();
//call build model
buildModel.eval(buildContextGenerator.getContext(children, advanceNodeIndex), bprobs);
double doneProb = bprobs[doneIndex];
if (debugOn) System.out.println("adi="+advanceNodeIndex+" "+advanceNode.getType()+"."+advanceNode.getLabel()+" "+advanceNode+" choose build="+(1-doneProb)+" attach="+doneProb);
if (1-doneProb > q) {
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);
if (!tag.equals(DONE)) {
Parse newParse1 = (Parse) p.clone();
Parse newNode = new Parse(p.getText(),advanceNode.getSpan(),tag,bprob,advanceNode.getHead());
newParse1.insert(newNode);
newParse1.addProb(Math.log(bprob));
newParsesList.add(newParse1);
if (checkComplete) {
cprobs = checkModel.eval(checkContextGenerator.getContext(newNode,children,advanceNodeIndex,false));
if (debugOn) System.out.println("building "+tag+" "+bprob+" c="+cprobs[completeIndex]);
if (cprobs[completeIndex] > probMass) { //just complete advances
setComplete(newNode);
newParse1.addProb(Math.log(cprobs[completeIndex]));
if (debugOn) System.out.println("Only advancing complete node");
}
else if (1-cprobs[completeIndex] > probMass) { //just incomplete advances
setIncomplete(newNode);
newParse1.addProb(Math.log(1-cprobs[completeIndex]));
if (debugOn) System.out.println("Only advancing incomplete node");
}
else { //both complete and incomplete advance
if (debugOn) System.out.println("Advancing both complete and incomplete nodes");
setComplete(newNode);
newParse1.addProb(Math.log(cprobs[completeIndex]));
Parse newParse2 = (Parse) p.clone();
Parse newNode2 = new Parse(p.getText(),advanceNode.getSpan(),tag,bprob,advanceNode.getHead());
newParse2.insert(newNode2);
newParse2.addProb(Math.log(bprob));
newParsesList.add(newParse2);
newParse2.addProb(Math.log(1-cprobs[completeIndex]));
setIncomplete(newNode2); //set incomplete for non-clone
}
}
else {
if (debugOn) System.out.println("building "+tag+" "+bprob);
}
}
}
}
//advance attaches
if (doneProb > q) {
Parse newParse1 = (Parse) p.clone(); //clone parse
//mark nodes as built
if (checkComplete) {
if (isComplete(advanceNode)) {
newParse1.setChild(originalAdvanceIndex,Parser.BUILT+"."+Parser.COMPLETE); //replace constituent being labeled to create new derivation
}
else {
newParse1.setChild(originalAdvanceIndex,Parser.BUILT+"."+Parser.INCOMPLETE); //replace constituent being labeled to create new derivation
}
}
else {
newParse1.setChild(originalAdvanceIndex,Parser.BUILT); //replace constituent being labeled to create new derivation
}
newParse1.addProb(Math.log(doneProb));
if (advanceNodeIndex == 0) { //no attach if first node.
newParsesList.add(newParse1);
}
else {
List rf = getRightFrontier(p,punctSet);
for (int fi=0,fs=rf.size();fi threshold and
// if !checkComplete then prevent daughter attaching to chunk
// if checkComplete then prevent daughter attacing to complete node or
// sister attaching to an incomplete node
if (prob > q && (
(!checkComplete && (attachments[ai]!= daughterAttachIndex || !isComplete(fn)))
||
(checkComplete && ((attachments[ai]== daughterAttachIndex && !isComplete(fn)) || (attachments[ai] == sisterAttachIndex && isComplete(fn)))))) {
Parse newParse2 = newParse1.cloneRoot(fn,originalZeroIndex);
Parse[] newKids = Parser.collapsePunctuation(newParse2.getChildren(),punctSet);
//remove node from top level since were going to attach it (including punct)
for (int ri=originalZeroIndex+1;ri<=originalAdvanceIndex;ri++) {
//System.out.println(at"-removing "+(originalZeroIndex+1)+" "+newParse2.getChildren()[originalZeroIndex+1]);
newParse2.remove(originalZeroIndex+1);
}
List crf = getRightFrontier(newParse2,punctSet);
Parse updatedNode;
if (attachments[ai] == daughterAttachIndex) {//attach daughter
updatedNode = (Parse) crf.get(fi);
updatedNode.add(advanceNode,headRules);
}
else { //attach sister
Parse psite;
if (fi+1 < crf.size()) {
psite = (Parse) crf.get(fi+1);
updatedNode = psite.adjoin(advanceNode,headRules);
}
else {
psite = newParse2;
updatedNode = psite.adjoinRoot(advanceNode,headRules,originalZeroIndex);
newKids[0] = updatedNode;
}
}
//update spans affected by attachment
for (int ni=fi+1;ni probMass) {
setComplete(updatedNode);
newParse2.addProb(Math.log(cprobs[completeIndex]));
if (debugOn) System.out.println("Only advancing complete node");
}
else if (1-cprobs[completeIndex] > probMass) {
setIncomplete(updatedNode);
newParse2.addProb(Math.log(1-cprobs[completeIndex]));
if (debugOn) System.out.println("Only advancing incomplete node");
}
else {
setComplete(updatedNode);
Parse newParse3 = newParse2.cloneRoot(updatedNode,originalZeroIndex);
newParse3.addProb(Math.log(cprobs[completeIndex]));
newParsesList.add(newParse3);
setIncomplete(updatedNode);
newParse2.addProb(Math.log(1-cprobs[completeIndex]));
if (debugOn) System.out.println("Advancing both complete and incomplete nodes; c="+cprobs[completeIndex]);
}
}
}
else {
if (debugOn) System.out.println("Skipping "+fn.getType()+"."+fn.getLabel()+" "+fn+" daughter="+(attachments[ai] == daughterAttachIndex)+" complete="+isComplete(fn)+" prob="+prob);
}
}
if(checkComplete && !isComplete(fn)) {
if (debugOn) System.out.println("Stopping at incomplete node("+fi+"): "+fn.getType()+"."+fn.getLabel()+" "+fn);
break;
}
}
}
}
Parse[] newParses = new Parse[newParsesList.size()];
newParsesList.toArray(newParses);
return newParses;
}
protected void advanceTop(Parse p) {
p.setType(TOP_NODE);
}
public static ParserModel train(String languageCode,
ObjectStream parseSamples, HeadRules rules, int iterations, int cut)
throws IOException {
// TODO: training code should be shared between two parsers
System.err.println("Building dictionary");
Dictionary mdict = buildDictionary(parseSamples, rules, cut);
parseSamples.reset();
// tag
POSModel posModel = POSTaggerME.train(languageCode, new PosSampleStream(
parseSamples), ModelType.MAXENT, null, null, cut, iterations);
parseSamples.reset();
// chunk
ChunkerModel chunkModel = ChunkerME.train(languageCode, new ChunkSampleStream(
parseSamples), cut, iterations, new ChunkContextGenerator());
parseSamples.reset();
// build
System.err.println("Training builder");
opennlp.model.EventStream bes = new ParserEventStream(parseSamples, rules,
ParserEventTypeEnum.BUILD, mdict);
AbstractModel buildModel = train(bes, iterations, cut);
parseSamples.reset();
// check
System.err.println("Training checker");
opennlp.model.EventStream kes = new ParserEventStream(parseSamples, rules,
ParserEventTypeEnum.CHECK);
AbstractModel checkModel = train(kes, iterations, cut);
parseSamples.reset();
// attach
System.err.println("Training attacher");
opennlp.model.EventStream attachEvents = new ParserEventStream(parseSamples, rules,
ParserEventTypeEnum.ATTACH);
AbstractModel attachModel = train(attachEvents, iterations, cut);
// TODO: Remove cast for HeadRules
return new ParserModel(languageCode, buildModel, checkModel,
attachModel, posModel, chunkModel,
(opennlp.tools.parser.lang.en.HeadRules) rules, ParserType.TREEINSERT);
}
@Deprecated
public static AbstractModel train(opennlp.model.EventStream es, int iterations, int cut) throws java.io.IOException {
return opennlp.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
}
@Deprecated
private static void usage() {
System.err.println("Usage: ParserME -[dict|tag|chunk|build|attach|fun] trainingFile parserModelDirectory [iterations cutoff]");
System.err.println();
System.err.println("Training file should be one sentence per line where each line consists of a Penn Treebank Style parse");
System.err.println("-tag Just build the tagging model.");
System.err.println("-chunk Just build the chunking model.");
System.err.println("-build Just build the build model");
System.err.println("-attach Just build the attach model");
System.err.println("-fun Predict function tags");
}
@Deprecated
public static void main(String[] args) throws java.io.IOException {
if (args.length < 3) {
usage();
System.exit(1);
}
boolean tag = false;
boolean chunk = false;
boolean build = false;
boolean attach = false;
boolean check = false;
boolean fun = false;
boolean all = true;
int argIndex = 0;
while (args[argIndex].startsWith("-")) {
all = false;
if (args[argIndex].equals("-tag")) {
tag = true;
}
else if (args[argIndex].equals("-chunk")) {
chunk = true;
}
else if (args[argIndex].equals("-build")) {
build = true;
}
else if (args[argIndex].equals("-attach")) {
attach = true;
}
else if (args[argIndex].equals("-check")) {
check = true;
}
else if (args[argIndex].equals("-fun")) {
fun = true;
}
else if (args[argIndex].equals("--")) {
argIndex++;
break;
}
else {
System.err.println("Invalid option " + args[argIndex]);
usage();
System.exit(1);
}
argIndex++;
}
java.io.File inFile = new java.io.File(args[argIndex++]);
String modelDirectory = args[argIndex++];
HeadRules rules = new opennlp.tools.parser.lang.en.HeadRules(modelDirectory+"/head_rules");
java.io.File tagFile = new java.io.File(modelDirectory+"/tag.bin.gz");
java.io.File chunkFile = new java.io.File(modelDirectory+"/chunk.bin.gz");
java.io.File buildFile = new java.io.File(modelDirectory+"/build.bin.gz");
java.io.File attachFile = new java.io.File(modelDirectory+"/attach.bin.gz");
java.io.File checkFile = new java.io.File(modelDirectory+"/check.bin.gz");
int iterations = 100;
int cutoff = 5;
if (args.length > argIndex) {
iterations = Integer.parseInt(args[argIndex++]);
cutoff = Integer.parseInt(args[argIndex++]);
}
if (fun) {
Parse.useFunctionTags(true);
}
if (tag || all) {
System.err.println("Training tagger");
opennlp.model.EventStream tes = new ParserEventStream(new ParseSampleStream(new PlainTextByLineStream(new java.io.FileReader(inFile))), rules, ParserEventTypeEnum.TAG);
AbstractModel tagModel = train(tes, iterations, cutoff);
System.out.println("Saving the tagger model as: " + tagFile);
new opennlp.maxent.io.SuffixSensitiveGISModelWriter(tagModel, tagFile).persist();
}
if (chunk || all) {
System.err.println("Training chunker");
opennlp.model.EventStream ces = new ParserEventStream(new ParseSampleStream(new PlainTextByLineStream(new java.io.FileReader(inFile))), rules, ParserEventTypeEnum.CHUNK);
AbstractModel chunkModel = train(ces, iterations, cutoff);
System.out.println("Saving the chunker model as: " + chunkFile);
new opennlp.maxent.io.SuffixSensitiveGISModelWriter(chunkModel, chunkFile).persist();
}
if (build || all) {
System.err.println("Training builder");
opennlp.model.EventStream bes = new ParserEventStream(new ParseSampleStream(new PlainTextByLineStream(new java.io.FileReader(inFile))), rules, ParserEventTypeEnum.BUILD,null);
AbstractModel buildModel = train(bes, iterations, cutoff);
System.out.println("Saving the build model as: " + buildFile);
new opennlp.maxent.io.SuffixSensitiveGISModelWriter(buildModel, buildFile).persist();
}
if (attach || all) {
System.err.println("Training attacher");
opennlp.model.EventStream kes = new ParserEventStream(new ParseSampleStream(new PlainTextByLineStream(new java.io.FileReader(inFile))), rules, ParserEventTypeEnum.ATTACH);
AbstractModel attachModel = train(kes, iterations, cutoff);
System.out.println("Saving the attach model as: " + attachFile);
new opennlp.maxent.io.SuffixSensitiveGISModelWriter(attachModel, attachFile).persist();
}
if (check || all) {
System.err.println("Training checker");
opennlp.model.EventStream ces = new ParserEventStream(new ParseSampleStream(new PlainTextByLineStream(new java.io.FileReader(inFile))), rules, ParserEventTypeEnum.CHECK);
AbstractModel checkModel = train(ces, iterations, cutoff);
System.out.println("Saving the check model as: " + checkFile);
new opennlp.maxent.io.SuffixSensitiveGISModelWriter(checkModel, checkFile).persist();
}
}
}
