Tokenizes the text. This component started as a PTB-style tokenizer, but was extended since then to handle both other languages and noisy web-style text. The tokenizer saves the character offsets of each token in the input text, as CharacterOffsetBeginAnnotation and CharacterOffsetEndAnnotation.

Property name Annotator class name Generated Annotation
tokenize TokenizerAnnotator TokensAnnotation (list of tokens), and CharacterOffsetBeginAnnotation, CharacterOffsetEndAnnotation, TextAnnotation (for each token)


Option name Type Default Description
tokenize.language Enum { English, French, German, Spanish, Unspecified, Whitespace } Unspecified Use the appropriate tokenizer for the given language. If the tokenizer is Unspecified, it defaults to using the English PTBTokenizer.
tokenize.class class name null If non-null, use this class as the Tokenizer. In general, you can now more easily do this by specifying a language to the TokenizerAnnotator.
tokenize.whitespace boolean false If set to true, separates words only when whitespace is encountered.
tokenize.keepeol boolean false If true, end-of-line tokens are kept and used as sentence boundaries with the WhitespaceTokenizer.
tokenize.options String null Accepts the options of PTBTokenizer for example, things like “americanize=false” or “strictTreebank3=true,untokenizable=allKeep”. See the PTBTokenizer documentation.
tokenize.verbose boolean false Make the TokenizerAnnotator verbose - that is, it prints out all tokenizations it performs.

Command line

Here are some example usages at the command line

Here is a basic example.

java -Xmx1g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit -file example.txt -outputFormat text

If run on example.txt

Joe Smith was born in California.
It will probably rain on Friday.

You should get this output in example.txt.out

Document: ID=example.txt (2 sentences, 14 tokens)

Sentence #1 (7 tokens):
Joe Smith was born in California.

[Text=Joe CharacterOffsetBegin=0 CharacterOffsetEnd=3]
[Text=Smith CharacterOffsetBegin=4 CharacterOffsetEnd=9]
[Text=was CharacterOffsetBegin=10 CharacterOffsetEnd=13]
[Text=born CharacterOffsetBegin=14 CharacterOffsetEnd=18]
[Text=in CharacterOffsetBegin=19 CharacterOffsetEnd=21]
[Text=California CharacterOffsetBegin=22 CharacterOffsetEnd=32]
[Text=. CharacterOffsetBegin=32 CharacterOffsetEnd=33]

Sentence #2 (7 tokens):
It will probably rain on Friday.

[Text=It CharacterOffsetBegin=34 CharacterOffsetEnd=36]
[Text=will CharacterOffsetBegin=37 CharacterOffsetEnd=41]
[Text=probably CharacterOffsetBegin=42 CharacterOffsetEnd=50]
[Text=rain CharacterOffsetBegin=51 CharacterOffsetEnd=55]
[Text=on CharacterOffsetBegin=56 CharacterOffsetEnd=58]
[Text=Friday CharacterOffsetBegin=59 CharacterOffsetEnd=65]
[Text=. CharacterOffsetBegin=65 CharacterOffsetEnd=66]

Java API

Here is a demo Java class that will run tokenization on text. It demonstrates both the new Core Wrapper API and the older Annotation API.

package edu.stanford.nlp.examples;

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

import java.util.*;

public class PipelineDemo {

  public static void main(String[] args) {
    // set up pipeline properties
    Properties props = new Properties();
    props.setProperty("annotators", "tokenize,ssplit");
    // set up pipeline
    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
    // the following has examples for the new Core Wrapper API and the older Annotation API
    // example using Core Wrappers (new API designed to make it easier to work with NLP data)
    System.out.println("Accessing Tokens In A CoreDocument");
    System.out.println("(text, char offset begin, char offset end)");
    CoreDocument exampleDocument = new CoreDocument("Here is the text to tokenize.");
    // annotate document
    // access tokens from a CoreDocument
    // a token is represented by a CoreLabel
    List<CoreLabel> firstSentenceTokens = exampleDocument.sentences().get(0).tokens();
    // this for loop will print out all of the tokens and the character offset info
    for (CoreLabel token : firstSentenceTokens) {
      System.out.println(token.word() + "\t" + token.beginPosition() + "\t" + token.endPosition());
    // example using older Annotation API
    System.out.println("Accessing Tokens In An Annotation");
    System.out.println("(text, char offset begin, char offset end)");
    Annotation exampleAnnotation = new Annotation("Here is the text to tokenize.");
    CoreMap firstSentence = exampleAnnotation.get(CoreAnnotations.SentencesAnnotation.class).get(0);
    // this for loop will print out all of the tokens and the character offset info
    for (CoreLabel token : firstSentence.get(CoreAnnotations.TokensAnnotation.class)) {
      System.out.println(token.word() + "\t" + token.beginPosition() + "\t" + token.endPosition());

More information

The tokenizer is described in detail on the Stanford NLP website.