nltk.stem.cistem module

class nltk.stem.cistem.Cistem

Bases: StemmerI

CISTEM Stemmer for German

This is the official Python implementation of the CISTEM stemmer. 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: https://www.cis.lmu.de/~weissweiler/cistem/

In the paper, we conducted an analysis of publicly available stemmers, developed two gold standards for German stemming and evaluated the stemmers based on the two gold standards. We 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.

case_insensitive is a a boolean specifying if case-insensitive stemming should be used. Case insensitivity 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 setting case_insensitive for false.

Parameters:

case_insensitive (bool) – if True, the stemming is case insensitive. False by default.

__init__(case_insensitive: bool = False)

Parameters:

case_insensitive (bool)

repl_xx = re.compile('(.)\\1')

repl_xx_back = re.compile('(.)\\*')

static replace_back(word: str) → str

Parameters:

word (str)

Return type:

str

static replace_to(word: str) → str

Parameters:

word (str)

Return type:

str

segment(word: str) → Tuple[str, str]

This method works very similarly to stem (:func:’cistem.stem’). The 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.

Parameters:

word (str) – The word that is to be stemmed.

Returns:

A tuple of the stemmed word and the removed suffix.

Return type:

Tuple[str, str]

>>> from nltk.stem.cistem import Cistem
>>> stemmer = Cistem()
>>> s1 = "Speicherbehältern"
>>> stemmer.segment(s1)
('speicherbehält', 'ern')
>>> s2 = "Grenzpostens"
>>> stemmer.segment(s2)
('grenzpost', 'ens')
>>> s3 = "Ausgefeiltere"
>>> stemmer.segment(s3)
('ausgefeilt', 'ere')
>>> stemmer = Cistem(True)
>>> stemmer.segment(s1)
('speicherbehäl', 'tern')
>>> stemmer.segment(s2)
('grenzpo', 'stens')
>>> stemmer.segment(s3)
('ausgefeil', 'tere')

stem(word: str) → str

Stems the input word.

Parameters:

word (str) – The word that is to be stemmed.

Returns:

The stemmed word.

Return type:

str

>>> from nltk.stem.cistem import Cistem
>>> stemmer = Cistem()
>>> s1 = "Speicherbehältern"
>>> stemmer.stem(s1)
'speicherbehalt'
>>> s2 = "Grenzpostens"
>>> stemmer.stem(s2)
'grenzpost'
>>> s3 = "Ausgefeiltere"
>>> stemmer.stem(s3)
'ausgefeilt'
>>> stemmer = Cistem(True)
>>> stemmer.stem(s1)
'speicherbehal'
>>> stemmer.stem(s2)
'grenzpo'
>>> stemmer.stem(s3)
'ausgefeil'

strip_emr = re.compile('e[mr]$')

strip_esn = re.compile('[esn]$')

strip_ge = re.compile('^ge(.{4,})')

strip_nd = re.compile('nd$')

strip_t = re.compile('t$')