bo-graduation/nltk-book/pattern-master/pattern/text/fr/__init__.py

#### PATTERN | FR ##################################################################################
# -*- coding: utf-8 -*-
# Copyright (c) 2013 University of Antwerp, Belgium
# Copyright (c) 2013 St. Lucas University College of Art & Design, Antwerp.
# Author: Tom De Smedt <tom@organisms.be>
# License: BSD (see LICENSE.txt for details).
# http://www.clips.ua.ac.be/pages/pattern

####################################################################################################
# French linguistical tools using fast regular expressions.

from __future__ import unicode_literals
from __future__ import division

from builtins import str, bytes, dict, int
from builtins import map, zip, filter
from builtins import object, range

import os
import sys

try:
    MODULE = os.path.dirname(os.path.realpath(__file__))
except:
    MODULE = ""

sys.path.insert(0, os.path.join(MODULE, "..", "..", "..", ".."))

# Import parser base classes.
from pattern.text import (
    Lexicon, Model, Morphology, Context, Parser as _Parser, ngrams, pprint, commandline,
    PUNCTUATION
)
# Import parser universal tagset.
from pattern.text import (
    penntreebank2universal as _penntreebank2universal,
    PTB, PENN, UNIVERSAL,
    NOUN, VERB, ADJ, ADV, PRON, DET, PREP, ADP, NUM, CONJ, INTJ, PRT, PUNC, X
)
# Import parse tree base classes.
from pattern.text.tree import (
    Tree, Text, Sentence, Slice, Chunk, PNPChunk, Chink, Word, table,
    SLASH, WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA, AND, OR
)
# Import sentiment analysis base classes.
from pattern.text import (
    Sentiment as _Sentiment,
    NOUN, VERB, ADJECTIVE, ADVERB,
    MOOD, IRONY
)
# Import spelling base class.
from pattern.text import (
    Spelling
)
# Import verb tenses.
from pattern.text import (
    INFINITIVE, PRESENT, PAST, FUTURE,
    FIRST, SECOND, THIRD,
    SINGULAR, PLURAL, SG, PL,
    INDICATIVE, IMPERATIVE, SUBJUNCTIVE, CONDITIONAL,
    IMPERFECTIVE, PERFECTIVE, PROGRESSIVE,
    IMPERFECT, PRETERITE,
    PARTICIPLE, GERUND
)
# Import inflection functions.
from pattern.text.fr.inflect import (
    pluralize, singularize, NOUN, VERB, ADJECTIVE,
    verbs, conjugate, lemma, lexeme, tenses,
    predicative, attributive
)
# Import all submodules.
from pattern.text.fr import inflect

sys.path.pop(0)

#--- FRENCH PARSER ---------------------------------------------------------------------------------
# The French parser is based on Lefff (Lexique des Formes Fléchies du Français).
# Benoît Sagot, Lionel Clément, Érice Villemonte de la Clergerie, Pierre Boullier.
# The Lefff 2 syntactic lexicon for French: architecture, acquisition.
# http://alpage.inria.fr/~sagot/lefff-en.html

# For words in Lefff that can have different part-of-speech tags,
# we used Lexique to find the most frequent POS-tag:
# http://www.lexique.org/

_subordinating_conjunctions = set((
    "afin", "comme", "lorsque", "parce", "puisque", "quand", "que", "quoique", "si"
))


def penntreebank2universal(token, tag):
    """ Converts a Penn Treebank II tag to a universal tag.
        For example: comme/IN => comme/CONJ
    """
    if tag == "IN" and token.lower() in _subordinating_conjunctions:
        return CONJ
    return _penntreebank2universal(token, tag)

ABBREVIATIONS = set((
    "av.", "boul.", "C.-B.", "c.-à-d.", "ex.", "éd.", "fig.", "I.-P.-E.", "J.-C.",
    "Ltee.", "Ltée.", "M.", "Me.", "Mlle.", "Mlles.", "MM.", "N.-B.", "N.-É.", "p.",
    "S.B.E.", "Ste.", "T.-N.", "t.a.b."
))

# While contractions in English are optional,
# they are required in French:
replacements = {
       "l'": "l' ",  # le/la
       "c'": "c' ",  # ce
       "d'": "d' ",  # de
       "j'": "j' ",  # je
       "m'": "m' ",  # me
       "n'": "n' ",  # ne
      "qu'": "qu' ", # que
       "s'": "s' ",  # se
       "t'": "t' ",  # te
   "jusqu'": "jusqu' ",
  "lorsqu'": "lorsqu' ",
  "puisqu'": "puisqu' ",
    # Same rule for Unicode apostrophe, see also Parser.find_tokens():
    r"(l|c|d|j|m|n|qu|s|t|jusqu|lorsqu|puisqu)’": "\\1&rsquo; "
}
replacements.update(((k.upper(), v.upper()) for k, v in list(replacements.items())))


def find_lemmata(tokens):
    """ Annotates the tokens with lemmata for plural nouns and conjugated verbs,
        where each token is a [word, part-of-speech] list.
    """
    for token in tokens:
        word, pos, lemma = token[0], token[1], token[0]
        if pos.startswith(("DT", "PR", "WP")):
            lemma = singularize(word, pos=pos)
        if pos.startswith(("RB", "IN")) and (word.endswith(("'", "’")) or word == "du"):
            lemma = singularize(word, pos=pos)
        if pos.startswith(("JJ",)):
            lemma = predicative(word)
        if pos == "NNS":
            lemma = singularize(word)
        if pos.startswith(("VB", "MD")):
            lemma = conjugate(word, INFINITIVE) or word
        token.append(lemma.lower())
    return tokens


class Parser(_Parser):

    def find_tokens(self, tokens, **kwargs):
        kwargs.setdefault("abbreviations", ABBREVIATIONS)
        kwargs.setdefault("replace", replacements)
        s = _Parser.find_tokens(self, tokens, **kwargs)
        s = [s.replace("&rsquo ;", "’") if isinstance(s, str) else s for s in s]
        return s

    def find_lemmata(self, tokens, **kwargs):
        return find_lemmata(tokens)

    def find_tags(self, tokens, **kwargs):
        if kwargs.get("tagset") in (PENN, None):
            kwargs.setdefault("map", lambda token, tag: (token, tag))
        if kwargs.get("tagset") == UNIVERSAL:
            kwargs.setdefault("map", lambda token, tag: penntreebank2universal(token, tag))
        return _Parser.find_tags(self, tokens, **kwargs)


class Sentiment(_Sentiment):

    def load(self, path=None):
        _Sentiment.load(self, path)
        # Map "précaire" to "precaire" (without diacritics, +1% accuracy).
        if not path:
            for w, pos in list(dict.items(self)):
                w0 = w
                if not w.endswith(("à", "è", "é", "ê", "ï")):
                    w = w.replace("à", "a")
                    w = w.replace("é", "e")
                    w = w.replace("è", "e")
                    w = w.replace("ê", "e")
                    w = w.replace("ï", "i")
                if w != w0:
                    for pos, (p, s, i) in pos.items():
                        self.annotate(w, pos, p, s, i)

parser = Parser(
     lexicon = os.path.join(MODULE, "fr-lexicon.txt"),
   frequency = os.path.join(MODULE, "fr-frequency.txt"),
  morphology = os.path.join(MODULE, "fr-morphology.txt"),
     context = os.path.join(MODULE, "fr-context.txt"),
     default = ("NN", "NNP", "CD"),
    language = "fr"
)

lexicon = parser.lexicon # Expose lexicon.

sentiment = Sentiment(
        path = os.path.join(MODULE, "fr-sentiment.xml"),
      synset = None,
   negations = ("n'", "ne", "ni", "non", "pas", "rien", "sans", "aucun", "jamais"),
   modifiers = ("RB",),
   modifier = lambda w: w.endswith("ment"),
   tokenizer = parser.find_tokens,
    language = "fr"
)

spelling = Spelling(
        path = os.path.join(MODULE, "fr-spelling.txt")
)


def tokenize(s, *args, **kwargs):
    """ Returns a list of sentences, where punctuation marks have been split from words.
    """
    return parser.find_tokens(s, *args, **kwargs)


def parse(s, *args, **kwargs):
    """ Returns a tagged Unicode string.
    """
    return parser.parse(s, *args, **kwargs)


def parsetree(s, *args, **kwargs):
    """ Returns a parsed Text from the given string.
    """
    return Text(parse(s, *args, **kwargs))


def tree(s, token=[WORD, POS, CHUNK, PNP, REL, LEMMA]):
    """ Returns a parsed Text from the given parsed string.
    """
    return Text(s, token)


def tag(s, tokenize=True, encoding="utf-8", **kwargs):
    """ Returns a list of (token, tag)-tuples from the given string.
    """
    tags = []
    for sentence in parse(s, tokenize, True, False, False, False, encoding, **kwargs).split():
        for token in sentence:
            tags.append((token[0], token[1]))
    return tags


def keywords(s, top=10, **kwargs):
    """ Returns a sorted list of keywords in the given string.
    """
    return parser.find_keywords(s, **dict({
        "frequency": parser.frequency,
              "top": top,
              "pos": ("NN",),
           "ignore": ("rt",)}, **kwargs))


def suggest(w):
    """ Returns a list of (word, confidence)-tuples of spelling corrections.
    """
    return spelling.suggest(w)


def polarity(s, **kwargs):
    """ Returns the sentence polarity (positive/negative) between -1.0 and 1.0.
    """
    return sentiment(s, **kwargs)[0]


def subjectivity(s, **kwargs):
    """ Returns the sentence subjectivity (objective/subjective) between 0.0 and 1.0.
    """
    return sentiment(s, **kwargs)[1]


def positive(s, threshold=0.1, **kwargs):
    """ Returns True if the given sentence has a positive sentiment (polarity >= threshold).
    """
    return polarity(s, **kwargs) >= threshold

split = tree # Backwards compatibility.

#---------------------------------------------------------------------------------------------------
# python -m pattern.fr xml -s "C'est l'exception qui confirme la règle." -OTCL

if __name__ == "__main__":
    commandline(parse)