pipeline/nltk/stem/porter.py

"""
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

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

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

__docformat__ = "plaintext"

import re

from nltk.stem.api import StemmerI


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 https://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

        An 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:

        https://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

        An 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)

        An 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
    compatibility 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):
        r"""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' occurrences, 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 (
            len(word) >= 2
            and 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)]
        )

    def stem(self, word, to_lowercase=True):
        """
        :param to_lowercase: if `to_lowercase=True` the word always lowercase
        """
        stem = word.lower() if to_lowercase else word

        if self.mode == self.NLTK_EXTENSIONS and word in self.pool:
            return self.pool[stem]

        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 stem

        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>"


def demo():
    """
    A demonstration of the porter stemmer on a sample from
    the Penn Treebank corpus.
    """

    from nltk import stem
    from nltk.corpus import treebank

    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)