nltk.stem package

Submodules

nltk.stem.api module

class nltk.stem.api.StemmerI

Bases: builtins.object

A processing interface for removing morphological affixes from words. This process is known as stemming.

stem(token)

Strip affixes from the token and return the stem.

Parameters:token (str) – The token that should be stemmed.

nltk.stem.isri module

ISRI Arabic Stemmer

The algorithm for this stemmer is described in:

Taghva, K., Elkoury, R., and Coombs, J. 2005. Arabic Stemming without a root dictionary. Information Science Research Institute. University of Nevada, Las Vegas, USA.

The Information Science Research Institute’s (ISRI) Arabic stemmer shares many features with the Khoja stemmer. However, the main difference is that ISRI stemmer does not use root dictionary. Also, if a root is not found, ISRI stemmer returned normalized form, rather than returning the original unmodified word.

Additional adjustments were made to improve the algorithm:

1- Adding 60 stop words. 2- Adding the pattern (تفاعيل) to ISRI pattern set. 3- The step 2 in the original algorithm was normalizing all hamza. This step is discarded because it increases the word ambiguities and changes the original root.

class nltk.stem.isri.ISRIStemmer

Bases: nltk.stem.api.StemmerI

ISRI Arabic stemmer based on algorithm: Arabic Stemming without a root dictionary. Information Science Research Institute. University of Nevada, Las Vegas, USA.

A few minor modifications have been made to ISRI basic algorithm. See the source code of this module for more information.

isri.stem(token) returns Arabic root for the given token.

The ISRI Stemmer requires that all tokens have Unicode string types. If you use Python IDLE on Arabic Windows you have to decode text first using Arabic ‘1256’ coding.

end_w5()

ending step (word of length five)

end_w6()

ending step (word of length six)

norm(num)

normalization: num=1 normalize diacritics num=2 normalize initial hamza num=3 both 1&2

pre1()

normalize short prefix

pre32()

remove length three and length two prefixes in this order

pro_w4()

process length four patterns and extract length three roots

pro_w53()

process length five patterns and extract length three roots

pro_w54()

process length five patterns and extract length four roots

pro_w6()

process length six patterns and extract length three roots

pro_w64()

process length six patterns and extract length four roots

stem(token)

Stemming a word token using the ISRI stemmer.

suf1()

normalize short sufix

suf32()

remove length three and length two suffixes in this order

waw()

remove connective ‘و’ if it precedes a word beginning with ‘و’

nltk.stem.lancaster module

A word stemmer based on the Lancaster stemming algorithm. Paice, Chris D. “Another Stemmer.” ACM SIGIR Forum 24.3 (1990): 56-61.

class nltk.stem.lancaster.LancasterStemmer

Bases: nltk.stem.api.StemmerI

Lancaster Stemmer

>>> from nltk.stem.lancaster import LancasterStemmer
>>> st = LancasterStemmer()
>>> st.stem('maximum')     # Remove "-um" when word is intact
'maxim'
>>> st.stem('presumably')  # Don't remove "-um" when word is not intact
'presum'
>>> st.stem('multiply')    # No action taken if word ends with "-ply"
'multiply'
>>> st.stem('provision')   # Replace "-sion" with "-j" to trigger "j" set of rules
'provid'
>>> st.stem('owed')        # Word starting with vowel must contain at least 2 letters
'ow'
>>> st.stem('ear')         # ditto
'ear'
>>> st.stem('saying')      # Words starting with consonant must contain at least 3
'say'
>>> st.stem('crying')      #     letters and one of those letters must be a vowel
'cry'
>>> st.stem('string')      # ditto
'string'
>>> st.stem('meant')       # ditto
'meant'
>>> st.stem('cement')      # ditto
'cem'

parseRules(rule_tuple)

Validate the set of rules used in this stemmer.

rule_tuple = ('ai*2.', 'a*1.', 'bb1.', 'city3s.', 'ci2>', 'cn1t>', 'dd1.', 'dei3y>', 'deec2ss.', 'dee1.', 'de2>', 'dooh4>', 'e1>', 'feil1v.', 'fi2>', 'gni3>', 'gai3y.', 'ga2>', 'gg1.', 'ht*2.', 'hsiug5ct.', 'hsi3>', 'i*1.', 'i1y>', 'ji1d.', 'juf1s.', 'ju1d.', 'jo1d.', 'jeh1r.', 'jrev1t.', 'jsim2t.', 'jn1d.', 'j1s.', 'lbaifi6.', 'lbai4y.', 'lba3>', 'lbi3.', 'lib2l>', 'lc1.', 'lufi4y.', 'luf3>', 'lu2.', 'lai3>', 'lau3>', 'la2>', 'll1.', 'mui3.', 'mu*2.', 'msi3>', 'mm1.', 'nois4j>', 'noix4ct.', 'noi3>', 'nai3>', 'na2>', 'nee0.', 'ne2>', 'nn1.', 'pihs4>', 'pp1.', 're2>', 'rae0.', 'ra2.', 'ro2>', 'ru2>', 'rr1.', 'rt1>', 'rei3y>', 'sei3y>', 'sis2.', 'si2>', 'ssen4>', 'ss0.', 'suo3>', 'su*2.', 's*1>', 's0.', 'tacilp4y.', 'ta2>', 'tnem4>', 'tne3>', 'tna3>', 'tpir2b.', 'tpro2b.', 'tcud1.', 'tpmus2.', 'tpec2iv.', 'tulo2v.', 'tsis0.', 'tsi3>', 'tt1.', 'uqi3.', 'ugo1.', 'vis3j>', 'vie0.', 'vi2>', 'ylb1>', 'yli3y>', 'ylp0.', 'yl2>', 'ygo1.', 'yhp1.', 'ymo1.', 'ypo1.', 'yti3>', 'yte3>', 'ytl2.', 'yrtsi5.', 'yra3>', 'yro3>', 'yfi3.', 'ycn2t>', 'yca3>', 'zi2>', 'zy1s.')

stem(word)

Stem a word using the Lancaster stemmer.

unicode_repr()

nltk.stem.porter module

Porter Stemmer

This is the Porter stemming algorithm, ported to Python from the version coded up in ANSI C by the author. It follows the algorithm presented in

Porter, M. “An algorithm for suffix stripping.” Program 14.3 (1980): 130-137.

only differing from it at the points marked –DEPARTURE– and –NEW– below.

For a more faithful version of the Porter algorithm, see

http://www.tartarus.org/~martin/PorterStemmer/

Later additions:

June 2000

The ‘l’ of the ‘logi’ -> ‘log’ rule is put with the stem, so that short stems like ‘geo’ ‘theo’ etc work like ‘archaeo’ ‘philo’ etc.

This follows a suggestion of Barry Wilkins, research student at Birmingham.

February 2000

the cvc test for not dropping final -e now looks after vc at the beginning of a word, so are, eve, ice, ore, use keep final -e. In this test c is any consonant, including w, x and y. This extension was suggested by Chris Emerson.

-fully

-> -ful treated like -fulness -> -ful, and

-tionally -> -tion treated like -tional -> -tion

both in Step 2. These were suggested by Hiranmay Ghosh, of New Delhi.

Invariants proceed, succeed, exceed. Also suggested by Hiranmay Ghosh.

Additional modifications were made to incorperate this module into nltk. All such modifications are marked with “–NLTK–”. The nltk version of this module is maintained by the NLTK developers, and is available from <http://nltk.sourceforge.net>

class nltk.stem.porter.PorterStemmer

Bases: nltk.stem.api.StemmerI

A word stemmer based on the Porter stemming algorithm.

Porter, M. “An algorithm for suffix stripping.” Program 14.3 (1980): 130-137.

A few minor modifications have been made to Porter’s basic algorithm. See the source code of this module for more information.

The Porter Stemmer requires that all tokens have string types.

stem(word)

stem_word(p, i=0, j=None)

Returns the stem of p, or, if i and j are given, the stem of p[i:j+1].

unicode_repr()

nltk.stem.porter.demo()

A demonstration of the porter stemmer on a sample from the Penn Treebank corpus.

nltk.stem.regexp module

class nltk.stem.regexp.RegexpStemmer(regexp, min=0)

Bases: nltk.stem.api.StemmerI

A stemmer that uses regular expressions to identify morphological affixes. Any substrings that match the regular expressions will be removed.

>>> from nltk.stem import RegexpStemmer
>>> st = RegexpStemmer('ing$|s$|e$', min=4)
>>> st.stem('cars')
'car'
>>> st.stem('mass')
'mas'
>>> st.stem('was')
'was'
>>> st.stem('bee')
'bee'
>>> st.stem('compute')
'comput'

Parameters:

• regexp (str or regexp) – The regular expression that should be used to identify morphological affixes.
• min (int) – The minimum length of string to stem

stem(word)

unicode_repr()

nltk.stem.rslp module

class nltk.stem.rslp.RSLPStemmer

Bases: nltk.stem.api.StemmerI

A stemmer for Portuguese.

>>> from nltk.stem import RSLPStemmer
>>> st = RSLPStemmer()
>>> # opening lines of Erico Verissimo's "Música ao Longe"
>>> text = '''
... Clarissa risca com giz no quadro-negro a paisagem que os alunos
... devem copiar . Uma casinha de porta e janela , em cima duma
... coxilha .'''
>>> for token in text.split():
...     print(st.stem(token))
clariss risc com giz no quadro-negr a pais que os alun dev copi .
uma cas de port e janel , em cim dum coxilh .

apply_rule(word, rule_index)

read_rule(filename)

stem(word)

nltk.stem.snowball module

Snowball stemmers and appendant demo function

This module provides a port of the Snowball stemmers developed by Martin Porter. There is also a demo function demonstrating the different algorithms. It can be invoked directly on the command line. For more information take a look into the class SnowballStemmer.

class nltk.stem.snowball.DanishStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._ScandinavianStemmer

The Danish Snowball stemmer.

Variables:

• __vowels – The Danish vowels.
• __consonants – The Danish consonants.
• __double_consonants – The Danish double consonants.
• __s_ending – Letters that may directly appear before a word final ‘s’.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.

Note:

A detailed description of the Danish stemming algorithm can be found under http://snowball.tartarus.org/algorithms/danish/stemmer.html

stem(word)

Stem a Danish word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.DutchStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The Dutch Snowball stemmer.

Variables:

• __vowels – The Dutch vowels.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step3b_suffixes – Suffixes to be deleted in step 3b of the algorithm.

Note:

A detailed description of the Dutch stemming algorithm can be found under http://snowball.tartarus.org/algorithms/dutch/stemmer.html

stem(word)

Stem a Dutch word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.EnglishStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The English Snowball stemmer.

Variables:

• __vowels – The English vowels.
• __double_consonants – The English double consonants.
• __li_ending – Letters that may directly appear before a word final ‘li’.
• __step0_suffixes – Suffixes to be deleted in step 0 of the algorithm.
• __step1a_suffixes – Suffixes to be deleted in step 1a of the algorithm.
• __step1b_suffixes – Suffixes to be deleted in step 1b of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.
• __step4_suffixes – Suffixes to be deleted in step 4 of the algorithm.
• __step5_suffixes – Suffixes to be deleted in step 5 of the algorithm.
• __special_words – A dictionary containing words which have to be stemmed specially.

Note:

A detailed description of the English stemming algorithm can be found under http://snowball.tartarus.org/algorithms/english/stemmer.html

stem(word)

Stem an English word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.FinnishStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The Finnish Snowball stemmer.

Variables:

• __vowels – The Finnish vowels.
• __restricted_vowels – A subset of the Finnish vowels.
• __long_vowels – The Finnish vowels in their long forms.
• __consonants – The Finnish consonants.
• __double_consonants – The Finnish double consonants.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.
• __step4_suffixes – Suffixes to be deleted in step 4 of the algorithm.

Note:

A detailed description of the Finnish stemming algorithm can be found under http://snowball.tartarus.org/algorithms/finnish/stemmer.html

stem(word)

Stem a Finnish word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.FrenchStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The French Snowball stemmer.

Variables:

• __vowels – The French vowels.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2a_suffixes – Suffixes to be deleted in step 2a of the algorithm.
• __step2b_suffixes – Suffixes to be deleted in step 2b of the algorithm.
• __step4_suffixes – Suffixes to be deleted in step 4 of the algorithm.

Note:

A detailed description of the French stemming algorithm can be found under http://snowball.tartarus.org/algorithms/french/stemmer.html

stem(word)

Stem a French word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.GermanStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The German Snowball stemmer.

Variables:

• __vowels – The German vowels.
• __s_ending – Letters that may directly appear before a word final ‘s’.
• __st_ending – Letter that may directly appear before a word final ‘st’.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.

Note:

A detailed description of the German stemming algorithm can be found under http://snowball.tartarus.org/algorithms/german/stemmer.html

stem(word)

Stem a German word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.HungarianStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._LanguageSpecificStemmer

The Hungarian Snowball stemmer.

Variables:

• __vowels – The Hungarian vowels.
• __digraphs – The Hungarian digraphs.
• __double_consonants – The Hungarian double consonants.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.
• __step4_suffixes – Suffixes to be deleted in step 4 of the algorithm.
• __step5_suffixes – Suffixes to be deleted in step 5 of the algorithm.
• __step6_suffixes – Suffixes to be deleted in step 6 of the algorithm.
• __step7_suffixes – Suffixes to be deleted in step 7 of the algorithm.
• __step8_suffixes – Suffixes to be deleted in step 8 of the algorithm.
• __step9_suffixes – Suffixes to be deleted in step 9 of the algorithm.

Note:

A detailed description of the Hungarian stemming algorithm can be found under http://snowball.tartarus.org/algorithms/hungarian/stemmer.html

stem(word)

Stem an Hungarian word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.ItalianStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The Italian Snowball stemmer.

Variables:

• __vowels – The Italian vowels.
• __step0_suffixes – Suffixes to be deleted in step 0 of the algorithm.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.

Note:

A detailed description of the Italian stemming algorithm can be found under http://snowball.tartarus.org/algorithms/italian/stemmer.html

stem(word)

Stem an Italian word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.NorwegianStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._ScandinavianStemmer

The Norwegian Snowball stemmer.

Variables:

• __vowels – The Norwegian vowels.
• __s_ending – Letters that may directly appear before a word final ‘s’.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.

Note:

A detailed description of the Norwegian stemming algorithm can be found under http://snowball.tartarus.org/algorithms/norwegian/stemmer.html

stem(word)

Stem a Norwegian word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.PorterStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._LanguageSpecificStemmer, nltk.stem.porter.PorterStemmer

A word stemmer based on the original Porter stemming algorithm.

Porter, M. “An algorithm for suffix stripping.” Program 14.3 (1980): 130-137.

A few minor modifications have been made to Porter’s basic algorithm. See the source code of the module nltk.stem.porter for more information.

class nltk.stem.snowball.PortugueseStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The Portuguese Snowball stemmer.

Variables:

• __vowels – The Portuguese vowels.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step4_suffixes – Suffixes to be deleted in step 4 of the algorithm.

Note:

A detailed description of the Portuguese stemming algorithm can be found under http://snowball.tartarus.org/algorithms/portuguese/stemmer.html

stem(word)

Stem a Portuguese word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.RomanianStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The Romanian Snowball stemmer.

Variables:

• __vowels – The Romanian vowels.
• __step0_suffixes – Suffixes to be deleted in step 0 of the algorithm.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.

Note:

A detailed description of the Romanian stemming algorithm can be found under http://snowball.tartarus.org/algorithms/romanian/stemmer.html

stem(word)

Stem a Romanian word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.RussianStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._LanguageSpecificStemmer

The Russian Snowball stemmer.

Variables:

• __perfective_gerund_suffixes – Suffixes to be deleted.
• __adjectival_suffixes – Suffixes to be deleted.
• __reflexive_suffixes – Suffixes to be deleted.
• __verb_suffixes – Suffixes to be deleted.
• __noun_suffixes – Suffixes to be deleted.
• __superlative_suffixes – Suffixes to be deleted.
• __derivational_suffixes – Suffixes to be deleted.

Note:

A detailed description of the Russian stemming algorithm can be found under http://snowball.tartarus.org/algorithms/russian/stemmer.html

stem(word)

Stem a Russian word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.SnowballStemmer(language, ignore_stopwords=False)

Bases: nltk.stem.api.StemmerI

Snowball Stemmer

At the moment, this port is able to stem words from fourteen languages: Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish and Swedish.

Furthermore, there is also the original English Porter algorithm:

Porter, M. “An algorithm for suffix stripping.” Program 14.3 (1980): 130-137.

The algorithms have been developed by Martin Porter. These stemmers are called Snowball, because he invented a programming language with this name for creating new stemming algorithms. There is more information available at http://snowball.tartarus.org/

The stemmer is invoked as shown below:

>>> from nltk.stem import SnowballStemmer
>>> print(" ".join(SnowballStemmer.languages)) # See which languages are supported
danish dutch english finnish french german hungarian
italian norwegian porter portuguese romanian russian
spanish swedish
>>> stemmer = SnowballStemmer("german") # Choose a language
>>> stemmer.stem("Autobahnen") # Stem a word
'autobahn'

Invoking the stemmers that way is useful if you do not know the language to be stemmed at runtime. Alternatively, if you already know the language, then you can invoke the language specific stemmer directly:

>>> from nltk.stem.snowball import GermanStemmer
>>> stemmer = GermanStemmer()
>>> stemmer.stem("Autobahnen")
'autobahn'

Create a language specific instance of the Snowball stemmer.

Parameters:

• language (str or unicode) – The language whose subclass is instantiated.
• ignore_stopwords (bool) – If set to True, stopwords are not stemmed and returned unchanged. Set to False by default.

Raises ValueError:  

If there is no stemmer for the specified language, a ValueError is raised.

languages = ('danish', 'dutch', 'english', 'finnish', 'french', 'german', 'hungarian', 'italian', 'norwegian', 'porter', 'portuguese', 'romanian', 'russian', 'spanish', 'swedish')

class nltk.stem.snowball.SpanishStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._StandardStemmer

The Spanish Snowball stemmer.

Variables:

• __vowels – The Spanish vowels.
• __step0_suffixes – Suffixes to be deleted in step 0 of the algorithm.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2a_suffixes – Suffixes to be deleted in step 2a of the algorithm.
• __step2b_suffixes – Suffixes to be deleted in step 2b of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.

Note:

A detailed description of the Spanish stemming algorithm can be found under http://snowball.tartarus.org/algorithms/spanish/stemmer.html

stem(word)

Stem a Spanish word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

class nltk.stem.snowball.SwedishStemmer(ignore_stopwords=False)

Bases: nltk.stem.snowball._ScandinavianStemmer

The Swedish Snowball stemmer.

Variables:

• __vowels – The Swedish vowels.
• __s_ending – Letters that may directly appear before a word final ‘s’.
• __step1_suffixes – Suffixes to be deleted in step 1 of the algorithm.
• __step2_suffixes – Suffixes to be deleted in step 2 of the algorithm.
• __step3_suffixes – Suffixes to be deleted in step 3 of the algorithm.

Note:

A detailed description of the Swedish stemming algorithm can be found under http://snowball.tartarus.org/algorithms/swedish/stemmer.html

stem(word)

Stem a Swedish word and return the stemmed form.

Parameters:word (str or unicode) – The word that is stemmed. Returns:The stemmed form. Return type:unicode

nltk.stem.snowball.demo()

This function provides a demonstration of the Snowball stemmers.

After invoking this function and specifying a language, it stems an excerpt of the Universal Declaration of Human Rights (which is a part of the NLTK corpus collection) and then prints out the original and the stemmed text.

nltk.stem.wordnet module

class nltk.stem.wordnet.WordNetLemmatizer

Bases: builtins.object

WordNet Lemmatizer

Lemmatize using WordNet’s built-in morphy function. Returns the input word unchanged if it cannot be found in WordNet.

>>> from nltk.stem import WordNetLemmatizer
>>> wnl = WordNetLemmatizer()
>>> print(wnl.lemmatize('dogs'))
dog
>>> print(wnl.lemmatize('churches'))
church
>>> print(wnl.lemmatize('aardwolves'))
aardwolf
>>> print(wnl.lemmatize('abaci'))
abacus
>>> print(wnl.lemmatize('hardrock'))
hardrock

lemmatize(word, pos='n')

unicode_repr()

nltk.stem.wordnet.teardown_module(module=None)

Module contents

NLTK Stemmers

Interfaces used to remove morphological affixes from words, leaving only the word stem. Stemming algorithms aim to remove those affixes required for eg. grammatical role, tense, derivational morphology leaving only the stem of the word. This is a difficult problem due to irregular words (eg. common verbs in English), complicated morphological rules, and part-of-speech and sense ambiguities (eg. ceil- is not the stem of ceiling).

StemmerI defines a standard interface for stemmers.