edu.stanford.nlp.process.stattok

Class StatTokSent

java.lang.Object

edu.stanford.nlp.process.stattok.StatTokSent

public class StatTokSent
extends java.lang.Object

The statistical tokenizer implementation.
It produces an UD compliant tokenization given a pre-trained UD model file, and a file containing multi-word tokens that have to be treated with rules after tokenization.
It reads raw text from CoreAnnotations.textAnnotation, and returns sentences and tokens in the form: List<List<CoreLabel>>
Each List<CoreLabel> object contains Tokens Annotation for a single sentence.

Author:

Alessandro Bondielli

Field Summary

Fields

Modifier and Type	Field and Description
static java.lang.String	SENTINEL

Constructor Summary

Constructors

Constructor and Description
StatTokSent(java.lang.String modelFile)
StatTokSent(java.lang.String modelFile, java.lang.String multiWordRulesFile) This is the constructor for the StatTokSent object.

Method Summary

Modifier and Type	Method and Description
static void	main(java.lang.String[] args)
java.util.List<java.util.List<CoreLabel>>	tokenize(java.lang.String text) The core tokenization function called via the statistical tokenizer annotator.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

SENTINEL

public static final java.lang.String SENTINEL

See Also:

Constant Field Values

Constructor Detail

StatTokSent

public StatTokSent(java.lang.String modelFile,
                   java.lang.String multiWordRulesFile)

This is the constructor for the StatTokSent object. Parameters: modelFile: a string containing the path to the model; multiWordRulesFile: a string containing the path to the file with multi-word tokens.

StatTokSent

public StatTokSent(java.lang.String modelFile)

Method Detail

tokenize

public java.util.List<java.util.List<CoreLabel>> tokenize(java.lang.String text)

The core tokenization function called via the statistical tokenizer annotator. Given a text and window size as input, returns a List<List<CoreLabel>> corresponding to sentences and tokens within each sentence. First, features are extracted for each character of the text, and the model is used to classify each character as:
S: begin of sentence;
T: begin of token;
C: begin of part in a multi-word token;
I: inside of a token;
O: outside any token.
Given the classification, characters are joined in tokens. For each sentence, tokens further analyzed to identify possibily misclassified multi-word tokens via rules, and indexed.

main

public static void main(java.lang.String[] args)
                 throws java.lang.Exception

Throws:

java.lang.Exception