Lingua::Stem::Cistem - CISTEM Stemmer for German


        use Lingua::Stem::Cistem;
        my $stemmed_word = Lingua::Stem::Cistem::stem($word);
        my @segments     = Lingua::Stem::Cistem::segment($word);
        use Lingua::Stem::Cistem qw(:orig);
        my $stemmed_word = stem($word);
        my @segments     = segment($word);
        use Lingua::Stem::Cistem qw(:robust);
        my $stemmed_word = stem_robust($word);
        my @segments     = segment_robust($word);


    This is the CISTEM stemmer for German based on the "OFFICIAL

    Typically stemmers are used in applications like Information Retrieval,
    Keyword Extraction or Topic Matching.

    It applies the CISTEM stemming algorithm to a word, returning the stem
    of this word.

    Now (2019) CISTEM has the best f-score compared to other stemmers for
    German on CPAN, while being one of the fastest.

    The methods in this package keep their original logic and API, only the
    module name changed from Cistem to Lingua::Stem::Cistem.

    Changes in this distribution applied to the "OFFICIAL IMPLEMENTATION":

    packaged for and released on CPAN

    use strict, use warnings

    the method "stem" is 6-9 % faster, "segment" keeps the speed

    undefined parameter word defaults to the empty string ''

    provides two additional methods "stem_robust" and "segment_robust" with
    the same logic as the official ones, but more robust against low
    quality input. "stem_robust" is ~45% and "segment_robust" ~70 slower.

    Since Version 0.02 the methods "stem_robust" and "segment_robust"
    support a third parameter $keep_ge_prefix. Default is is the previous
    behavior, i.e. remove the prefix 'ge'.


    It is based on the paper

        Leonie Weißweiler, Alexander Fraser (2017).
        Developing a Stemmer for German Based on a Comparative Analysis of Publicly Available Stemmers.
        In Proceedings of the German Society for Computational Linguistics and Language Technology (GSCL)

    which can be read here:

    In the paper, the authors conducted an analysis of publicly available
    stemmers, developed two gold standards for German stemming and
    evaluated the stemmers based on the two gold standards. They then
    proposed the stemmer implemented here and show that it achieves
    slightly better f-measure than the other stemmers and is thrice as fast
    as the Snowball stemmer for German while being about as fast as most
    other stemmers.

    Source repository


    Lingua::Stem::Cistem exports no subroutines per default to avoid
    conflicts with other stemmers.

    You can either use the methods without importing the subroutines

        use Lingua::Stem::Cistem;
        my $stem = Lingua::Stem::Cistem::stem($word);

    or import some or all of the methods:

        use Lingua::Stem::Cistem qw(stem segment);
        my $stem = stem($word);
        my @segments = segment($word);
        use Lingua::Stem::Cistem qw(:all);
        my $stem = stem($word);


        :all    - imports stem segment stem_robust segment_robust
        :orig   - imports stem segment
        :robust - imports              stem_robust segment_robust


          stem($word, $case_insensitivity)

      This method takes the word to be stemmed and a boolean specifiying if
      case-insensitive stemming should be used and returns the stemmed
      word. If only the word is passed to the method or the second
      parameter is 0, normal case-sensitive stemming is used, if the second
      parameter is 1, case-insensitive stemming is used.

      Case sensitivity improves performance only if words in the text may
      be incorrectly upper case. For all-lowercase and correctly cased
      text, best performance is achieved by using the case-sensitive


          stem_robust($word, $case_insensitivity, $keep_ge_prefix)

      This method works like "stem" with the following differences for

      German Umlauts in decomposed normalization form (NFD) work like
      composed (NFC) ones.

      Other characters plus combining characters are treated as graphemes,
      i.e. with length 1 instead of 2 or more, which has an influence on
      the resulting stem.

      The characters $, %, & keep their value, i.e. they roundtrip.

      If parameter $keep_ge_prefix is set, prefix 'ge' is kept in the stem.
      Be careful if this really improves the results. Mostly removing 'ge'
      performs better.

      This should not be necessary, if the input is carefully normalized,
      tokenized, and filtered.


          segment($word, $case_insensitivity)

      This method works very similarly to stem. The only difference is that
      in addition to returning the stem, it also returns the rest that was
      removed at the end. To be able to return the stem unchanged so the
      stem and the rest can be concatenated to form the original word, all
      subsitutions that altered the stem in any other way than by removing
      letters at the end were left out.

              my ($stem, $suffix) = segment($word);


          segment_robust($word, $case_insensitivity, $keep_ge_prefix)

      This method works exactly like "stem_robust" and returns a list of
      prefix, stem and suffix:

              my ($prefix, $stem, $suffix) = segment_robust($word);


    Tests were run using the file goldstandard1.txt (317441 words, 3.76
    MB), which can be found here:

    The test iterates over the words in the file. Times measured include
    the overhead of startup and iteration.

        Platform (only one thread used)
        Intel Core i7-4770HQ Processor
        4 Cores, 8 Threads
        2.20 - 3.40 GHz
        6 MB Cache
        16GB DDR3 RAM
        MacOS Mojave Version 10.14.4
        Perl 5.20.1
     | source: goldstandard1.txt   | words: 317441                 |
     | method         | version    | duration | factor | words/sec |
     | stem           | official   |  2.862s  |  1.00  |  110916   |
     | stem           | this v0.01 |  2.678s  |  0.94  |  118536   |
     | stem_robust    | this v0.01 |  4.111s  |  1.44  |   77217   |
     |                |            |          |        |           |
     | segment        | official   |  2.594s  |  1.00  |  122375   |
     | segment        | this v0.01 |  2.642s  |  1.02  |  120151   |
     | segment_robust | this v0.01 |  4.368s  |  1.68  |   72674   |



    Helmut Wollmersdorfer <>


    Copyright 2019-2020 Helmut Wollmersdorfer Copyright 2017 Leonie
    Weißweiler (original version)


    This library is free software; you can redistribute it and/or modify it
    under the same terms as Perl itself.


    Lingua::Stem::Snowball, Lingua::Stem::UniNE, Lingua::Stem,