package edu.stanford.nlp.parser.nndep.demo;
import edu.stanford.nlp.ling.HasWord;
import edu.stanford.nlp.ling.TaggedWord;
import edu.stanford.nlp.parser.nndep.DependencyParser;
import edu.stanford.nlp.process.DocumentPreprocessor;
import edu.stanford.nlp.tagger.maxent.MaxentTagger;
import edu.stanford.nlp.trees.GrammaticalStructure;
import java.io.StringReader;
import java.util.List;
/**
* Demonstrates how to first use the tagger, then use the NN dependency
* parser. Note that the parser will not work on untagged text.
*
* @author Jon Gauthier
*/
public class DependencyParserDemo {
public static void main(String[] args) {
String modelPath = DependencyParser.DEFAULT_MODEL;
String taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger";
for (int argIndex = 0; argIndex < args.length; ) {
switch (args[argIndex]) {
case "-tagger":
taggerPath = args[argIndex + 1];
argIndex += 2;
break;
case "-model":
modelPath = args[argIndex + 1];
argIndex += 2;
break;
default:
throw new RuntimeException("Unknown argument " + args[argIndex]);
}
}
String text = "I can almost always tell when movies use fake dinosaurs.";
MaxentTagger tagger = new MaxentTagger(taggerPath);
DependencyParser parser = DependencyParser.loadFromModelFile(modelPath);
DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text));
for (List<HasWord> sentence : tokenizer) {
List<TaggedWord> tagged = tagger.tagSentence(sentence);
GrammaticalStructure gs = parser.predict(tagged);
// Print typed dependencies
System.err.println(gs);
}
}
}