Generating annotations

The backbone of the CoreNLP package is formed by two classes: Annotation and Annotator. Annotations are the data structure which hold the results of annotators. Annotations are basically maps, from keys to bits of the annotation, such as the parse, the part-of-speech tags, or named entity tags. Annotators are a lot like functions, except that they operate over Annotations instead of Objects. They do things like tokenize, parse, or NER tag sentences. Annotators and Annotations are integrated by AnnotationPipelines, which create sequences of generic Annotators. Stanford CoreNLP inherits from the AnnotationPipeline class, and is customized with NLP Annotators.

The Annotators currently supported and the Annotations they generate are summarized here.

To construct a Stanford CoreNLP object from a given set of properties, use StanfordCoreNLP(Properties props). This method creates the pipeline using the annotators given in the “annotators” property (see below for an example setting). The complete list of accepted annotator names is listed in the first column of the table here. To parse an arbitrary text, use the annotate(Annotation document) method.

import edu.stanford.nlp.pipeline.*;
import java.util.*;

public class BasicPipelineExample {

    public static void main(String[] args) {

        // creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution
        Properties props = new Properties();
        props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

        // read some text in the text variable
        String text = "...";

        // create an empty Annotation just with the given text
        Annotation document = new Annotation(text);

        // run all Annotators on this text



You can give other properties to CoreNLP by build a Properties object with more stuff in it. If you want to do that, you might find it conventient to use our PropertiesUtils.asProperties(String ...) method which will take a list of Strings that are alternately keys and values and build a Properties object:

// build pipeline
StanfordCoreNLP pipeline = new StanfordCoreNLP(
		"annotators", "tokenize,ssplit,pos,lemma,parse,natlog",
		"ssplit.isOneSentence", "true",
		"parse.model", "edu/stanford/nlp/models/srparser/englishSR.ser.gz",
		"tokenize.language", "en"));

// read some text in the text variable
String text = ... // Add your text here!
Annotation document = new Annotation(text);

// run all Annotators on this text

If you do not anticipate requiring extensive customization, consider using the Simple CoreNLP API.

Interpreting the output

The output of the Annotators is accessed using the data structures CoreMap and CoreLabel.

// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
List<CoreMap> 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
  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);

  // this is the parse tree of the current sentence
  Tree tree = sentence.get(TreeAnnotation.class);

  // this is the Stanford dependency graph of the current sentence
  SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);

// This is 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!
Map<Integer, CorefChain> graph =