Package opennlp.tools.tokenize

Interface Tokenizer

  • All Known Implementing Classes:
    SimpleTokenizer, TokenizerME, WhitespaceTokenizer, WordpieceTokenizer
    public interface Tokenizer
    The interface for tokenizers, which segment a string into its tokens.

    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.

    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 Tokenizer is now responsible to split those tokens correctly.

    In non-segmented languages like Chinese, tokenization is more difficult since words are not segmented by a whitespace.

    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.

    For most subsequent NLP tasks, it is desirable to over-tokenize rather than to under-tokenize.

    • Method Detail

      • tokenize

        String[] tokenize​(String s)
        Splits a string into its atomic parts.
        Parameters:
        s - The string to be tokenized.
        Returns:
        The String[] with the individual tokens as the array elements.

      • tokenizePos

        Span[] tokenizePos​(String s)
        Finds the boundaries of atomic parts in a string.
        Parameters:
        s - The string to be tokenized.
        Returns:
        The spans (offsets into {@code s}) for each token as the individuals array elements.