Source code for nltk.stem.porter

"""
Porter Stemmer

This is the Porter stemming algorithm. It follows the algorithm
presented in

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

with some optional deviations that can be turned on or off with the
`mode` argument to the constructor.

Martin Porter, the algorithm's inventor, maintains a web page about the
algorithm at

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

which includes another Python implementation and other implementations
in many languages.
"""

from __future__ import print_function, unicode_literals

__docformat__ = 'plaintext'

import re

from nltk.stem.api import StemmerI
from nltk.compat import python_2_unicode_compatible

@python_2_unicode_compatible
[docs]class PorterStemmer(StemmerI): """ A word stemmer based on the Porter stemming algorithm. Porter, M. "An algorithm for suffix stripping." Program 14.3 (1980): 130-137. See http://www.tartarus.org/~martin/PorterStemmer/ for the homepage of the algorithm. Martin Porter has endorsed several modifications to the Porter algorithm since writing his original paper, and those extensions are included in the implementations on his website. Additionally, others have proposed further improvements to the algorithm, including NLTK contributors. There are thus three modes that can be selected by passing the appropriate constant to the class constructor's `mode` attribute: PorterStemmer.ORIGINAL_ALGORITHM - Implementation that is faithful to the original paper. Note that Martin Porter has deprecated this version of the algorithm. Martin distributes implementations of the Porter Stemmer in many languages, hosted at: http://www.tartarus.org/~martin/PorterStemmer/ and all of these implementations include his extensions. He strongly recommends against using the original, published version of the algorithm; only use this mode if you clearly understand why you are choosing to do so. PorterStemmer.MARTIN_EXTENSIONS - Implementation that only uses the modifications to the algorithm that are included in the implementations on Martin Porter's website. He has declared Porter frozen, so the behaviour of those implementations should never change. PorterStemmer.NLTK_EXTENSIONS (default) - Implementation that includes further improvements devised by NLTK contributors or taken from other modified implementations found on the web. For the best stemming, you should use the default NLTK_EXTENSIONS version. However, if you need to get the same results as either the original algorithm or one of Martin Porter's hosted versions for compability with an existing implementation or dataset, you can use one of the other modes instead. """ # Modes the Stemmer can be instantiated in NLTK_EXTENSIONS = 'NLTK_EXTENSIONS' MARTIN_EXTENSIONS = 'MARTIN_EXTENSIONS' ORIGINAL_ALGORITHM = 'ORIGINAL_ALGORITHM' def __init__(self, mode=NLTK_EXTENSIONS): if mode not in ( self.NLTK_EXTENSIONS, self.MARTIN_EXTENSIONS, self.ORIGINAL_ALGORITHM ): raise ValueError( "Mode must be one of PorterStemmer.NLTK_EXTENSIONS, " "PorterStemmer.MARTIN_EXTENSIONS, or " "PorterStemmer.ORIGINAL_ALGORITHM" ) self.mode = mode if self.mode == self.NLTK_EXTENSIONS: # This is a table of irregular forms. It is quite short, # but still reflects the errors actually drawn to Martin # Porter's attention over a 20 year period! irregular_forms = { "sky" : ["sky", "skies"], "die" : ["dying"], "lie" : ["lying"], "tie" : ["tying"], "news" : ["news"], "inning" : ["innings", "inning"], "outing" : ["outings", "outing"], "canning" : ["cannings", "canning"], "howe" : ["howe"], "proceed" : ["proceed"], "exceed" : ["exceed"], "succeed" : ["succeed"], } self.pool = {} for key in irregular_forms: for val in irregular_forms[key]: self.pool[val] = key self.vowels = frozenset(['a', 'e', 'i', 'o', 'u']) def _is_consonant(self, word, i): """Returns True if word[i] is a consonant, False otherwise A consonant is defined in the paper as follows: A consonant in a word is a letter other than A, E, I, O or U, and other than Y preceded by a consonant. (The fact that the term `consonant' is defined to some extent in terms of itself does not make it ambiguous.) So in TOY the consonants are T and Y, and in SYZYGY they are S, Z and G. If a letter is not a consonant it is a vowel. """ if word[i] in self.vowels: return False if word[i] == 'y': if i == 0: return True else: return (not self._is_consonant(word, i - 1)) return True def _measure(self, stem): """Returns the 'measure' of stem, per definition in the paper From the paper: A consonant will be denoted by c, a vowel by v. A list ccc... of length greater than 0 will be denoted by C, and a list vvv... of length greater than 0 will be denoted by V. Any word, or part of a word, therefore has one of the four forms: CVCV ... C CVCV ... V VCVC ... C VCVC ... V These may all be represented by the single form [C]VCVC ... [V] where the square brackets denote arbitrary presence of their contents. Using (VC){m} to denote VC repeated m times, this may again be written as [C](VC){m}[V]. m will be called the \measure\ of any word or word part when represented in this form. The case m = 0 covers the null word. Here are some examples: m=0 TR, EE, TREE, Y, BY. m=1 TROUBLE, OATS, TREES, IVY. m=2 TROUBLES, PRIVATE, OATEN, ORRERY. """ cv_sequence = '' # Construct a string of 'c's and 'v's representing whether each # character in `stem` is a consonant or a vowel. # e.g. 'falafel' becomes 'cvcvcvc', # 'architecture' becomes 'vcccvcvccvcv' for i in range(len(stem)): if self._is_consonant(stem, i): cv_sequence += 'c' else: cv_sequence += 'v' # Count the number of 'vc' occurences, which is equivalent to # the number of 'VC' occurrences in Porter's reduced form in the # docstring above, which is in turn equivalent to `m` return cv_sequence.count('vc') def _has_positive_measure(self, stem): return self._measure(stem) > 0 def _contains_vowel(self, stem): """Returns True if stem contains a vowel, else False""" for i in range(len(stem)): if not self._is_consonant(stem, i): return True return False def _ends_double_consonant(self, word): """Implements condition *d from the paper Returns True if word ends with a double consonant """ return ( word[-1] == word[-2] and self._is_consonant(word, len(word)-1) ) def _ends_cvc(self, word): """Implements condition *o from the paper From the paper: *o - the stem ends cvc, where the second c is not W, X or Y (e.g. -WIL, -HOP). """ return ( len(word) >= 3 and self._is_consonant(word, len(word) - 3) and not self._is_consonant(word, len(word) - 2) and self._is_consonant(word, len(word) - 1) and word[-1] not in ('w', 'x', 'y') ) or ( self.mode == self.NLTK_EXTENSIONS and len(word) == 2 and not self._is_consonant(word, 0) and self._is_consonant(word, 1) ) def _replace_suffix(self, word, suffix, replacement): """Replaces `suffix` of `word` with `replacement""" assert word.endswith(suffix), "Given word doesn't end with given suffix" if suffix == '': return word + replacement else: return word[:-len(suffix)] + replacement def _apply_rule_list(self, word, rules): """Applies the first applicable suffix-removal rule to the word Takes a word and a list of suffix-removal rules represented as 3-tuples, with the first element being the suffix to remove, the second element being the string to replace it with, and the final element being the condition for the rule to be applicable, or None if the rule is unconditional. """ for rule in rules: suffix, replacement, condition = rule if suffix == '*d' and self._ends_double_consonant(word): stem = word[:-2] if condition is None or condition(stem): return stem + replacement else: # Don't try any further rules return word if word.endswith(suffix): stem = self._replace_suffix(word, suffix, '') if condition is None or condition(stem): return stem + replacement else: # Don't try any further rules return word return word def _step1a(self, word): """Implements Step 1a from "An algorithm for suffix stripping" From the paper: SSES -> SS caresses -> caress IES -> I ponies -> poni ties -> ti SS -> SS caress -> caress S -> cats -> cat """ # this NLTK-only rule extends the original algorithm, so # that 'flies'->'fli' but 'dies'->'die' etc if self.mode == self.NLTK_EXTENSIONS: if word.endswith('ies') and len(word) == 4: return self._replace_suffix(word, 'ies', 'ie') return self._apply_rule_list(word, [ ('sses', 'ss', None), # SSES -> SS ('ies', 'i', None), # IES -> I ('ss', 'ss', None), # SS -> SS ('s', '', None), # S -> ]) def _step1b(self, word): """Implements Step 1b from "An algorithm for suffix stripping" From the paper: (m>0) EED -> EE feed -> feed agreed -> agree (*v*) ED -> plastered -> plaster bled -> bled (*v*) ING -> motoring -> motor sing -> sing If the second or third of the rules in Step 1b is successful, the following is done: AT -> ATE conflat(ed) -> conflate BL -> BLE troubl(ed) -> trouble IZ -> IZE siz(ed) -> size (*d and not (*L or *S or *Z)) -> single letter hopp(ing) -> hop tann(ed) -> tan fall(ing) -> fall hiss(ing) -> hiss fizz(ed) -> fizz (m=1 and *o) -> E fail(ing) -> fail fil(ing) -> file The rule to map to a single letter causes the removal of one of the double letter pair. The -E is put back on -AT, -BL and -IZ, so that the suffixes -ATE, -BLE and -IZE can be recognised later. This E may be removed in step 4. """ # this NLTK-only block extends the original algorithm, so that # 'spied'->'spi' but 'died'->'die' etc if self.mode == self.NLTK_EXTENSIONS: if word.endswith('ied'): if len(word) == 4: return self._replace_suffix(word, 'ied', 'ie') else: return self._replace_suffix(word, 'ied', 'i') # (m>0) EED -> EE if word.endswith('eed'): stem = self._replace_suffix(word, 'eed', '') if self._measure(stem) > 0: return stem + 'ee' else: return word rule_2_or_3_succeeded = False for suffix in ['ed', 'ing']: if word.endswith(suffix): intermediate_stem = self._replace_suffix(word, suffix, '') if self._contains_vowel(intermediate_stem): rule_2_or_3_succeeded = True break if not rule_2_or_3_succeeded: return word return self._apply_rule_list(intermediate_stem, [ ('at', 'ate', None), # AT -> ATE ('bl', 'ble', None), # BL -> BLE ('iz', 'ize', None), # IZ -> IZE # (*d and not (*L or *S or *Z)) # -> single letter ( '*d', intermediate_stem[-1], lambda stem: intermediate_stem[-1] not in ('l', 's', 'z') ), # (m=1 and *o) -> E ( '', 'e', lambda stem: (self._measure(stem) == 1 and self._ends_cvc(stem)) ), ]) def _step1c(self, word): """Implements Step 1c from "An algorithm for suffix stripping" From the paper: Step 1c (*v*) Y -> I happy -> happi sky -> sky """ def nltk_condition(stem): """ This has been modified from the original Porter algorithm so that y->i is only done when y is preceded by a consonant, but not if the stem is only a single consonant, i.e. (*c and not c) Y -> I So 'happy' -> 'happi', but 'enjoy' -> 'enjoy' etc This is a much better rule. Formerly 'enjoy'->'enjoi' and 'enjoyment'->'enjoy'. Step 1c is perhaps done too soon; but with this modification that no longer really matters. Also, the removal of the contains_vowel(z) condition means that 'spy', 'fly', 'try' ... stem to 'spi', 'fli', 'tri' and conflate with 'spied', 'tried', 'flies' ... """ return len(stem) > 1 and self._is_consonant(stem, len(stem) - 1) def original_condition(stem): return self._contains_vowel(stem) return self._apply_rule_list(word, [ ( 'y', 'i', nltk_condition if self.mode == self.NLTK_EXTENSIONS else original_condition ) ]) def _step2(self, word): """Implements Step 2 from "An algorithm for suffix stripping" From the paper: Step 2 (m>0) ATIONAL -> ATE relational -> relate (m>0) TIONAL -> TION conditional -> condition rational -> rational (m>0) ENCI -> ENCE valenci -> valence (m>0) ANCI -> ANCE hesitanci -> hesitance (m>0) IZER -> IZE digitizer -> digitize (m>0) ABLI -> ABLE conformabli -> conformable (m>0) ALLI -> AL radicalli -> radical (m>0) ENTLI -> ENT differentli -> different (m>0) ELI -> E vileli - > vile (m>0) OUSLI -> OUS analogousli -> analogous (m>0) IZATION -> IZE vietnamization -> vietnamize (m>0) ATION -> ATE predication -> predicate (m>0) ATOR -> ATE operator -> operate (m>0) ALISM -> AL feudalism -> feudal (m>0) IVENESS -> IVE decisiveness -> decisive (m>0) FULNESS -> FUL hopefulness -> hopeful (m>0) OUSNESS -> OUS callousness -> callous (m>0) ALITI -> AL formaliti -> formal (m>0) IVITI -> IVE sensitiviti -> sensitive (m>0) BILITI -> BLE sensibiliti -> sensible """ if self.mode == self.NLTK_EXTENSIONS: # Instead of applying the ALLI -> AL rule after '(a)bli' per # the published algorithm, instead we apply it first, and, # if it succeeds, run the result through step2 again. if ( word.endswith('alli') and self._has_positive_measure( self._replace_suffix(word, 'alli', '') ) ): return self._step2( self._replace_suffix(word, 'alli', 'al') ) bli_rule = ('bli', 'ble', self._has_positive_measure) abli_rule = ('abli', 'able', self._has_positive_measure) rules = [ ('ational', 'ate', self._has_positive_measure), ('tional', 'tion', self._has_positive_measure), ('enci', 'ence', self._has_positive_measure), ('anci', 'ance', self._has_positive_measure), ('izer', 'ize', self._has_positive_measure), abli_rule if self.mode == self.ORIGINAL_ALGORITHM else bli_rule, ('alli', 'al', self._has_positive_measure), ('entli', 'ent', self._has_positive_measure), ('eli', 'e', self._has_positive_measure), ('ousli', 'ous', self._has_positive_measure), ('ization', 'ize', self._has_positive_measure), ('ation', 'ate', self._has_positive_measure), ('ator', 'ate', self._has_positive_measure), ('alism', 'al', self._has_positive_measure), ('iveness', 'ive', self._has_positive_measure), ('fulness', 'ful', self._has_positive_measure), ('ousness', 'ous', self._has_positive_measure), ('aliti', 'al', self._has_positive_measure), ('iviti', 'ive', self._has_positive_measure), ('biliti', 'ble', self._has_positive_measure), ] if self.mode == self.NLTK_EXTENSIONS: rules.append( ('fulli', 'ful', self._has_positive_measure) ) # 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. rules.append(( "logi", "log", lambda stem: self._has_positive_measure(word[:-3]) )) if self.mode == self.MARTIN_EXTENSIONS: rules.append( ("logi", "log", self._has_positive_measure) ) return self._apply_rule_list(word, rules) def _step3(self, word): """Implements Step 3 from "An algorithm for suffix stripping" From the paper: Step 3 (m>0) ICATE -> IC triplicate -> triplic (m>0) ATIVE -> formative -> form (m>0) ALIZE -> AL formalize -> formal (m>0) ICITI -> IC electriciti -> electric (m>0) ICAL -> IC electrical -> electric (m>0) FUL -> hopeful -> hope (m>0) NESS -> goodness -> good """ return self._apply_rule_list(word, [ ('icate', 'ic', self._has_positive_measure), ('ative', '', self._has_positive_measure), ('alize', 'al', self._has_positive_measure), ('iciti', 'ic', self._has_positive_measure), ('ical', 'ic', self._has_positive_measure), ('ful', '', self._has_positive_measure), ('ness', '', self._has_positive_measure), ]) def _step4(self, word): """Implements Step 4 from "An algorithm for suffix stripping" Step 4 (m>1) AL -> revival -> reviv (m>1) ANCE -> allowance -> allow (m>1) ENCE -> inference -> infer (m>1) ER -> airliner -> airlin (m>1) IC -> gyroscopic -> gyroscop (m>1) ABLE -> adjustable -> adjust (m>1) IBLE -> defensible -> defens (m>1) ANT -> irritant -> irrit (m>1) EMENT -> replacement -> replac (m>1) MENT -> adjustment -> adjust (m>1) ENT -> dependent -> depend (m>1 and (*S or *T)) ION -> adoption -> adopt (m>1) OU -> homologou -> homolog (m>1) ISM -> communism -> commun (m>1) ATE -> activate -> activ (m>1) ITI -> angulariti -> angular (m>1) OUS -> homologous -> homolog (m>1) IVE -> effective -> effect (m>1) IZE -> bowdlerize -> bowdler The suffixes are now removed. All that remains is a little tidying up. """ measure_gt_1 = lambda stem: self._measure(stem) > 1 return self._apply_rule_list(word, [ ('al', '', measure_gt_1), ('ance', '', measure_gt_1), ('ence', '', measure_gt_1), ('er', '', measure_gt_1), ('ic', '', measure_gt_1), ('able', '', measure_gt_1), ('ible', '', measure_gt_1), ('ant', '', measure_gt_1), ('ement', '', measure_gt_1), ('ment', '', measure_gt_1), ('ent', '', measure_gt_1), # (m>1 and (*S or *T)) ION -> ( 'ion', '', lambda stem: self._measure(stem) > 1 and stem[-1] in ('s', 't') ), ('ou', '', measure_gt_1), ('ism', '', measure_gt_1), ('ate', '', measure_gt_1), ('iti', '', measure_gt_1), ('ous', '', measure_gt_1), ('ive', '', measure_gt_1), ('ize', '', measure_gt_1), ]) def _step5a(self, word): """Implements Step 5a from "An algorithm for suffix stripping" From the paper: Step 5a (m>1) E -> probate -> probat rate -> rate (m=1 and not *o) E -> cease -> ceas """ # Note that Martin's test vocabulary and reference # implementations are inconsistent in how they handle the case # where two rules both refer to a suffix that matches the word # to be stemmed, but only the condition of the second one is # true. # Earlier in step2b we had the rules: # (m>0) EED -> EE # (*v*) ED -> # but the examples in the paper included "feed"->"feed", even # though (*v*) is true for "fe" and therefore the second rule # alone would map "feed"->"fe". # However, in THIS case, we need to handle the consecutive rules # differently and try both conditions (obviously; the second # rule here would be redundant otherwise). Martin's paper makes # no explicit mention of the inconsistency; you have to infer it # from the examples. # For this reason, we can't use _apply_rule_list here. if word.endswith('e'): stem = self._replace_suffix(word, 'e', '') if self._measure(stem) > 1: return stem if self._measure(stem) == 1 and not self._ends_cvc(stem): return stem return word def _step5b(self, word): """Implements Step 5a from "An algorithm for suffix stripping" From the paper: Step 5b (m > 1 and *d and *L) -> single letter controll -> control roll -> roll """ return self._apply_rule_list(word, [ ('ll', 'l', lambda stem: self._measure(word[:-1]) > 1) ])
[docs] def stem(self, word): stem = word.lower() if self.mode == self.NLTK_EXTENSIONS and word in self.pool: return self.pool[word] if self.mode != self.ORIGINAL_ALGORITHM and len(word) <= 2: # With this line, strings of length 1 or 2 don't go through # the stemming process, although no mention is made of this # in the published algorithm. return word stem = self._step1a(stem) stem = self._step1b(stem) stem = self._step1c(stem) stem = self._step2(stem) stem = self._step3(stem) stem = self._step4(stem) stem = self._step5a(stem) stem = self._step5b(stem) return stem
def __repr__(self): return '<PorterStemmer>'
[docs]def demo(): """ A demonstration of the porter stemmer on a sample from the Penn Treebank corpus. """ from nltk.corpus import treebank from nltk import stem stemmer = stem.PorterStemmer() orig = [] stemmed = [] for item in treebank.fileids()[:3]: for (word, tag) in treebank.tagged_words(item): orig.append(word) stemmed.append(stemmer.stem(word)) # Convert the results to a string, and word-wrap them. results = ' '.join(stemmed) results = re.sub(r"(.{,70})\s", r'\1\n', results+' ').rstrip() # Convert the original to a string, and word wrap it. original = ' '.join(orig) original = re.sub(r"(.{,70})\s", r'\1\n', original+' ').rstrip() # Print the results. print('-Original-'.center(70).replace(' ', '*').replace('-', ' ')) print(original) print('-Results-'.center(70).replace(' ', '*').replace('-', ' ')) print(results) print('*'*70)