/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License. You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


package opennlp.tools.tokenize;

import opennlp.tools.util.Span;

/**
 * The interface for tokenizers, which segment a string into its tokens.
 * <p>
 * Tokenization is a necessary step before more complex NLP tasks can be applied.
 * These usually process text on a token level. The quality of tokenization is
 * important because it influences the performance of high-level task applied to it.
 * <p>
 * In segmented languages like English most words are segmented by whitespaces
 * expect for punctuations, etc. which is directly attached to the word without a white space
 * in between, it is not possible to just split at all punctuations because in abbreviations dots
 * are a part of the token itself. A {@link Tokenizer} is now responsible to split those tokens
 * correctly.
 * <p>
 * In non-segmented languages like Chinese, tokenization is more difficult since words
 * are not segmented by a whitespace.
 * <p>
 * Tokenizers can also be used to segment already identified tokens further into more
 * atomic parts to get a deeper understanding. This approach helps more complex task
 * to gain insight into tokens which do not represent words like numbers, units or tokens
 * which are part of a special notation.
 * <p>
 * For most subsequent NLP tasks, it is desirable to over-tokenize rather than to under-tokenize.
 */
public interface Tokenizer {

  /**
   * Splits a string into its atomic parts.
   *
   * @param s The string to be tokenized.
   * @return  The String[] with the individual tokens as the array elements.
   */
  String[] tokenize(String s);

  /**
   * Finds the boundaries of atomic parts in a string.
   *
   * @param s The string to be tokenized.
   * @return The {@link Span spans (offsets into {@code s})} for each token as the individuals array elements.
   */
  Span[] tokenizePos(String s);

}
