.. Copyright (C) 2001-2012 NLTK Project
.. For license information, see LICENSE.TXT

==========
 Stemmers
==========

Overview
~~~~~~~~

Stemmers remove morphological affixes from words, leaving only the
word stem.

    >>> from nltk.stem import *

Unit tests for the Porter stemmer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    >>> from nltk.stem.porter import *

Create a new Porter stemmer.

    >>> stemmer = PorterStemmer()

Test the cons() (consonant) method.

    >>> stemmer.b = "ready"
    >>> stemmer.k = len("ready") - 1

    >>> bool(stemmer.cons(0))
    True

    >>> bool(stemmer.cons(1))
    False

    >>> bool(stemmer.cons(4))
    False

    >>> stemmer.b = "yield"
    >>> stemmer.k = len("yield") - 1

    >>> bool(stemmer.cons(0))
    True

    >>> stemmer.b = "abeyance"
    >>> stemmer.k = len("abeyance") - 1

    >>> bool(stemmer.cons(3))
    True

Test the m() (number of vowel/consonant sequences from the start of a word to
some offset) method.

    >>> stemmer.m()		# 0 offset into the string
    0

    >>> stemmer.j = stemmer.k	# Set the offset to be the final string char
    >>> stemmer.m()
    3

Test the vowelinstem() method (checks for a vowel within the first j chars).

    >>> stemmer.b = "ready"
    >>> stemmer.k = len("ready") - 1
    >>> stemmer.j = 0

    >>> stemmer.vowelinstem()
    0

    >>> stemmer.j = stemmer.k
    >>> stemmer.vowelinstem()
    1

Test the doublec() (identical double consonant) method.

    >>> stemmer.b = "riddle"
    >>> stemmer.k = len("riddle") - 1

    >>> stemmer.doublec(0)	# Can't use at the first char
    0

    >>> stemmer.doublec(4)	# Chars at j and j-1 not identical
    0

    >>> stemmer.doublec(3)
    1

Test the cvc() method.

    >>> stemmer.cvc(0)		# Can't use at the first char
    0

    >>> stemmer.cvc(1)		# Sequence not vowel, consonant
    0

    >>> stemmer.b = "away"


    >>> stemmer.cvc(1)
    1

    >>> stemmer.cvc(2)		# Sequence not consonant, vowel, consonant
    0

    >>> stemmer.cvc(3)		# Final consonant is a member of {'w', 'x', 'y'}
    0

    >>> stemmer.b = "trace"
    >>> stemmer.k = len("trace") - 1

    >>> stemmer.cvc(3)
    1

Test the ends() (end substring matching) method.

    >>> stemmer.ends("verylongstring")	# Supplied string longer than buffer
    0

    >>> stemmer.ends("ice")		# String doesn't match
    0

    >>> stemmer.ends("ace")
    1

Test the setto() method (replaces the suffix of the buffered string).

    >>> stemmer.j = 3
    >>> stemmer.setto("ing")
    >>> stemmer.b
    'tracing'

Test the stemmer on various pluralised words.

    >>> plurals = ['caresses', 'flies', 'dies', 'mules', 'denied',
    ...            'died', 'agreed', 'owned', 'humbled', 'sized',
    ...            'meeting', 'stating', 'siezing', 'itemization',
    ...            'sensational', 'traditional', 'reference', 'colonizer',
    ...            'plotted']
    >>> singles = []

    >>> for plural in plurals:
    ...     singles.append(stemmer.stem(plural))

    >>> singles # doctest: +NORMALIZE_WHITESPACE
    ['caress', 'fli', 'die', 'mule', 'deni', 'die', 'agre', 'own',
     'humbl', 'size', 'meet', 'state', 'siez', 'item', 'sensat',
     'tradit', 'refer', 'colon', 'plot']

Unit tests for Regexp stemmer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    >>> patterns = "ed$|ing$|able$|s$"

    >>> stemmer = RegexpStemmer(patterns, 4)

    >>> stemmer.stem("red")
    'red'

    >>> stemmer.stem("hurried")
    'hurri'

    >>> stemmer.stem("advisable")
    'advis'

    >>> stemmer.stem("impossible")
    'impossible'

Unit tests for Snowball stemmer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    >>> from nltk.stem.snowball import SnowballStemmer

See which languages are supported.

    >>> SnowballStemmer.languages # doctest: +NORMALIZE_WHITESPACE
    ('danish', 'dutch', 'english', 'finnish', 'french', 'german',
    'hungarian', 'italian', 'norwegian', 'porter', 'portuguese',
    'romanian', 'russian', 'spanish', 'swedish')

Create a new instance of a language specific subclass.

    >>> stemmer_german = SnowballStemmer("german")

Stem a word.

    >>> stemmer_german.stem(u"Schr\xe4nke")
    u'schrank'

Decide not to stem stopwords.

    >>> stemmer_german2 = SnowballStemmer("german", ignore_stopwords=True)
    >>> stemmer_german.stem(u"keinen")
    u'kein'
    >>> stemmer_german2.stem(u"keinen")
    u'keinen'

The 'english' stemmer is better than the original 'porter' stemmer.

    >>> SnowballStemmer("english").stem(u"generously")
    u'generous'
    >>> SnowballStemmer("porter").stem(u"generously")
    u'gener'

Russian words both consisting of Cyrillic and Roman letters can be stemmed.

    >>> stemmer_russian = SnowballStemmer("russian")
    >>> stemmer_russian.stem(u'\u0430\u0432\u0435\u043d\u0430\u043d\u0442\u043d'
    ...                      u'\u0435\u043d\u044c\u043a\u0430\u044f')
    u'\u0430\u0432\u0435\u043d\u0430\u043d\u0442\u043d\u0435\u043d\u044c\u043a'
    >>> stemmer_russian.stem(u"\u0430\u0432\u0435\u043d\u0430\u043d"
    ...                u"\u0442\u043d\u0435\u043d\u044c\u043a\u0430\u044f")
    u'\u0430\u0432\u0435\u043d\u0430\u043d\u0442\u043d\u0435\u043d\u044c\u043a'
    >>> stemmer_russian.stem(u"avenantnen'kai^a")
    u"avenantnen'k"
