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Sigma knowledge engineering system is an system for developing, viewing and debugging theories in first
order logic. It works with Knowledge Interchange Format (KIF) and is optimized for the Suggested Upper Merged
Ontology (SUMO) www.ontologyportal.org.
The newest version!
package com.articulate.sigma.nlp.pipeline;
/*
Copyright 2014-2015 IPsoft
Author: Andrei Holub [email protected]
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
*/
import com.articulate.sigma.nlp.constants.LangLib;
import com.google.common.collect.Iterables;
import com.google.common.collect.Lists;
import edu.stanford.nlp.dcoref.CorefChain;
import edu.stanford.nlp.dcoref.CorefCoreAnnotations.CorefChainAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreAnnotations.*;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeCoreAnnotations.KBestTreesAnnotation;
import edu.stanford.nlp.util.CoreMap;
import edu.stanford.nlp.util.ScoredObject;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import static edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations.*;
public class SentenceUtil {
/** ***************************************************************
* Print all the sentences in this document
* CoreMap is essentially a Map that uses class objects as keys and
* has values with custom types
*/
public static void printSentences(Annotation document) {
List sentences = document.get(SentencesAnnotation.class);
for(CoreMap sentence : sentences) {
// traversing the words in the current sentence
// a CoreLabel is a CoreMap with additional token-specific methods
int count = 1;
for (CoreLabel token : sentence.get(TokensAnnotation.class)) {
// this is the text of the token
String word = token.get(TextAnnotation.class);
// this is the POS tag of the token
String pos = token.get(PartOfSpeechAnnotation.class);
// this is the NER label of the token
String ne = token.get(NamedEntityTagAnnotation.class);
List entity = token.get(MentionsAnnotation.class);
System.out.println(word + "-" + count + "/" + pos + "/" + ne + "/" + entity);
count++;
}
// this is the parse tree of the current sentence
// Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
// this is the Stanford dependency graph of the current sentence
SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
System.out.println(dependencies.toList());
//System.out.println(dependencies.toPOSList());
System.out.println(getFullNamedEntities(sentence));
List> scoredTrees = sentence.get(KBestTreesAnnotation.class);
System.out.println("\nTree Scores:");
for (ScoredObject scoredTree : scoredTrees) {
//SemanticGraph graph = SemanticGraphFactory.generateUncollapsedDependencies(scoredTree.object());
System.out.println(scoredTree.score());
}
}
}
/** ***************************************************************
* @return a List of Strings which are concatenated tokens forming
* a single named entity, with the suffix of the number of the
* head. For example "I went to New York." would return
* "NewYork-4".
*/
public static ArrayList getFullNamedEntities (CoreMap sentence) {
ArrayList nes = new ArrayList();
StringBuffer ne = new StringBuffer();
String neType = "";
int count = 1;
int wordCount = 0; // number of words packed into a given ne
for (CoreLabel token : sentence.get(TokensAnnotation.class)) {
// this is the text of the token
String word = token.get(TextAnnotation.class);
System.out.println(word);
// this is the NER label of the token
String type = token.get(NamedEntityTagAnnotation.class);
if (neType == "") {
neType = type;
if (!type.equals("O"))
ne.append(word);
wordCount = 1;
}
else if (!neType.equals(type)) {
if (!neType.equals("O"))
nes.add(ne.toString() + "-" + (count-wordCount));
ne = new StringBuffer();
if (!type.equals("O")) {
ne.append(word);
wordCount = 1;
}
else
wordCount = 0;
neType = type;
}
else {
if (!type.equals("O"))
ne.append(word);
wordCount++;
}
System.out.println(word + "-" + count + "/" + type + "/" + neType + "/" + ne + "/" + wordCount);
count++;
}
return nes;
}
/** ***************************************************************
* Print the coreference link graph
* Each chain stores a set of mentions that link to each other,
* along with a method for getting the most representative mention
* Both sentence and token offsets start at 1!
*/
public static void printCorefChain(Annotation document) {
Map graph = document.get(CorefChainAnnotation.class);
for (CorefChain cc : graph.values()) {
List mentions = cc.getMentionsInTextualOrder();
if (mentions.size() > 1) {
for (CorefChain.CorefMention ment : mentions) {
System.out.println(ment.sentNum + " : " + ment.headIndex + " : " + ment.mentionSpan);
}
}
}
}
//TODO: I'm a monster! Refactor me
/** *************************************************************
* returns a list of strings that add tense, number, etc. information about words in input
* ex. tense(PAST, Verb)
* number(SINGULAR, Noun)
*/
public static List findPOSInformation(List tokens, List dependenciesList) {
List posInformation = Lists.newArrayList();
for (CoreLabel label : tokens) {
Pattern auxPattern = Pattern.compile("aux\\(.*, " + label.toString() + "\\)");
boolean isAux = false;
for (String dep : dependenciesList) {
if (auxPattern.matcher(dep).find()) {
isAux = true;
break;
}
}
if (!isAux) {
boolean progressive = false;
boolean perfect = false;
String pos = label.get(CoreAnnotations.PartOfSpeechAnnotation.class);
if (LangLib.POS_VBD.equals(pos)) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_PAST, label.toString()));
}
else if (LangLib.POS_VBP.equals(pos) || LangLib.POS_VBZ.equals(pos)) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_PRESENT, label.toString()));
}
else if (LangLib.POS_VBG.equals(pos) || LangLib.POS_VB.equals(pos) || LangLib.POS_VBN.equals(pos)) {
Pattern reverseAuxPattern = Pattern.compile("aux\\(" + label.toString() + ", .*-(\\d+)\\)");
for (String dep : dependenciesList) {
Matcher auxMatcher = reverseAuxPattern.matcher(dep);
if (auxMatcher.find()) {
int i = Integer.parseInt(auxMatcher.group(1));
CoreLabel t = tokens.get(i-1);
if (t.get(CoreAnnotations.LemmaAnnotation.class).equals("be")) {
if (t.get(CoreAnnotations.PartOfSpeechAnnotation.class).equals(LangLib.POS_VBP) || t.get(CoreAnnotations.PartOfSpeechAnnotation.class).equals(LangLib.POS_VBZ)) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_PRESENT, label.toString()));
}
else if (t.get(CoreAnnotations.PartOfSpeechAnnotation.class).equals(LangLib.POS_VBD)) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_PAST, label.toString()));
}
progressive = true;
}
else if (t.get(CoreAnnotations.LemmaAnnotation.class).equals("will")) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_FUTURE, label.toString()));
}
else if (t.get(CoreAnnotations.LemmaAnnotation.class).equals("have")) {
if (t.get(CoreAnnotations.PartOfSpeechAnnotation.class).equals(LangLib.POS_VBP) || t.get(CoreAnnotations.PartOfSpeechAnnotation.class).equals(LangLib.POS_VBZ)) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_PRESENT, label.toString()));
}
else if (t.get(CoreAnnotations.PartOfSpeechAnnotation.class).equals(LangLib.POS_VBD)) {
posInformation.add(makeBinaryRelationship("tense", LangLib.TENSE_PAST, label.toString()));
}
perfect = true;
}
}
}
}
else if (LangLib.POS_NN.equals(pos) || LangLib.POS_NNP.equals(pos)) {
posInformation.add(makeBinaryRelationship("number", LangLib.NUMBER_SINGULAR, label.toString()));
}
else if (LangLib.POS_NNS.equals(pos) || LangLib.POS_NNPS.equals(pos)) {
posInformation.add(makeBinaryRelationship("number", LangLib.NUMBER_PLURAL, label.toString()));
}
if (progressive && perfect) {
posInformation.add(makeBinaryRelationship("aspect", LangLib.ASPECT_PROGRESSIVE_PERFECT, label.toString()));
}
else if (progressive) {
posInformation.add(makeBinaryRelationship("aspect", LangLib.ASPECT_PROGRESSIVE, label.toString()));
}
else if (perfect) {
posInformation.add(makeBinaryRelationship("aspect", LangLib.ASPECT_PERFECT, label.toString()));
}
}
}
return posInformation;
}
//TODO: see if this exists somewhere else or move to utility class
/** *************************************************************
*/
public static String makeBinaryRelationship(String relationship, String argument1, String argument2) {
StringBuilder sb = new StringBuilder(relationship);
sb.append("(");
sb.append(argument1);
sb.append(", ");
sb.append(argument2);
sb.append(")");
return sb.toString();
}
public static CoreMap getLastSentence(Annotation document) {
List sentences = document.get(SentencesAnnotation.class);
if(!sentences.isEmpty()) {
return Iterables.getLast(sentences);
} else {
return null;
}
}
/** ***************************************************************
*/
public static ArrayList toDependenciesList(Annotation document) {
return toDependenciesList(document.get(SentencesAnnotation.class));
}
/** ***************************************************************
*/
public static ArrayList toDependenciesList(List sentences) {
ArrayList results = Lists.newArrayList();
for (CoreMap sentence : sentences) {
SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
results = Lists.newArrayList(dependencies.toList().split("\n"));
}
return results;
}
/** ***************************************************************
*/
public static void main(String[] args) {
Annotation a = Pipeline.toAnnotation("I went to New York and had cookies and cream in the Empire State Building in January with Mary.");
printCorefChain(a);
printSentences(a);
}
}