lib/StanfordCoreNlpDemo.java

package edu.stanford.nlp.pipeline.demo;

import java.io.*;
import java.util.*;

import edu.stanford.nlp.hcoref.data.CorefChain;
import edu.stanford.nlp.hcoref.CorefCoreAnnotations;
import edu.stanford.nlp.io.*;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.util.*;

/** This class demonstrates building and using a Stanford CoreNLP pipeline. */
public class StanfordCoreNlpDemo {

  /** Usage: java -cp "*" StanfordCoreNlpDemo [inputFile [outputTextFile [outputXmlFile]]] */
  public static void main(String[] args) throws IOException {
    // set up optional output files
    PrintWriter out;
    if (args.length > 1) {
      out = new PrintWriter(args[1]);
    } else {
      out = new PrintWriter(System.out);
    }
    PrintWriter xmlOut = null;
    if (args.length > 2) {
      xmlOut = new PrintWriter(args[2]);
    }

    // Create a CoreNLP pipeline. To build the default pipeline, you can just use:
    //   StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
    // Here's a more complex setup example:
    //   Properties props = new Properties();
    //   props.put("annotators", "tokenize, ssplit, pos, lemma, ner, depparse");
    //   props.put("ner.model", "edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz");
    //   props.put("ner.applyNumericClassifiers", "false");
    //   StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

    // Add in sentiment
    Properties props = new Properties();
    props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref, sentiment");

    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

    // Initialize an Annotation with some text to be annotated. The text is the argument to the constructor.
    Annotation annotation;
    if (args.length > 0) {
      annotation = new Annotation(IOUtils.slurpFileNoExceptions(args[0]));
    } else {
      annotation = new Annotation("Kosgi Santosh sent an email to Stanford University. He didn't get a reply.");
    }

    // run all the selected Annotators on this text
    pipeline.annotate(annotation);

    // this prints out the results of sentence analysis to file(s) in good formats
    pipeline.prettyPrint(annotation, out);
    if (xmlOut != null) {
      pipeline.xmlPrint(annotation, xmlOut);
    }

    // Access the Annotation in code
    // The toString() method on an Annotation just prints the text of the Annotation
    // But you can see what is in it with other methods like toShorterString()
    out.println();
    out.println("The top level annotation");
    out.println(annotation.toShorterString());
    out.println();

    // An Annotation is a Map with Class keys for the linguistic analysis types.
    // You can get and use the various analyses individually.
    // For instance, this gets the parse tree of the first sentence in the text.
    List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
    if (sentences != null && ! sentences.isEmpty()) {
      CoreMap sentence = sentences.get(0);
      out.println("The keys of the first sentence's CoreMap are:");
      out.println(sentence.keySet());
      out.println();
      out.println("The first sentence is:");
      out.println(sentence.toShorterString());
      out.println();
      out.println("The first sentence tokens are:");
      for (CoreMap token : sentence.get(CoreAnnotations.TokensAnnotation.class)) {
        out.println(token.toShorterString());
      }
      Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
      out.println();
      out.println("The first sentence parse tree is:");
      tree.pennPrint(out);
      out.println();
      out.println("The first sentence basic dependencies are:");
      out.println(sentence.get(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class).toString(SemanticGraph.OutputFormat.LIST));
      out.println("The first sentence collapsed, CC-processed dependencies are:");
      SemanticGraph graph = sentence.get(SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation.class);
      out.println(graph.toString(SemanticGraph.OutputFormat.LIST));

      // Access coreference. In the coreference link graph,
      // each chain stores a set of mentions that co-refer with each other,
      // along with a method for getting the most representative mention.
      // Both sentence and token offsets start at 1!
      out.println("Coreference information");
      Map<Integer, CorefChain> corefChains =
          annotation.get(CorefCoreAnnotations.CorefChainAnnotation.class);
      if (corefChains == null) { return; }
      for (Map.Entry<Integer,CorefChain> entry: corefChains.entrySet()) {
        out.println("Chain " + entry.getKey() + " ");
        for (CorefChain.CorefMention m : entry.getValue().getMentionsInTextualOrder()) {
          // We need to subtract one since the indices count from 1 but the Lists start from 0
          List<CoreLabel> tokens = sentences.get(m.sentNum - 1).get(CoreAnnotations.TokensAnnotation.class);
          // We subtract two for end: one for 0-based indexing, and one because we want last token of mention not one following.
          out.println("  " + m + ", i.e., 0-based character offsets [" + tokens.get(m.startIndex - 1).beginPosition() +
                  ", " + tokens.get(m.endIndex - 2).endPosition() + ")");
        }
      }
      out.println();

      out.println("The first sentence overall sentiment rating is " + sentence.get(SentimentCoreAnnotations.SentimentClass.class));
    }
    IOUtils.closeIgnoringExceptions(out);
    IOUtils.closeIgnoringExceptions(xmlOut);
  }

}