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Python

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#### PATTERN | ES ##################################################################################
# -*- coding: utf-8 -*-
# Copyright (c) 2012 University of Antwerp, Belgium
# Author: Tom De Smedt <tom@organisms.be>
# License: BSD (see LICENSE.txt for details).
# http://www.clips.ua.ac.be/pages/pattern
####################################################################################################
# Spanish 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,
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 spelling base class.
from pattern.text import (
Spelling
)
# Import verb tenses.
from pattern.text import (
INFINITIVE, PRESENT, PAST, FUTURE, CONDITIONAL,
FIRST, SECOND, THIRD,
SINGULAR, PLURAL, SG, PL,
INDICATIVE, IMPERATIVE, SUBJUNCTIVE,
IMPERFECTIVE, PERFECTIVE, PROGRESSIVE,
IMPERFECT, PRETERITE,
PARTICIPLE, GERUND
)
# Import inflection functions.
from pattern.text.es.inflect import (
article, referenced, DEFINITE, INDEFINITE,
MASCULINE, MALE, FEMININE, FEMALE, NEUTER, NEUTRAL, PLURAL, M, F, N, PL,
pluralize, singularize, NOUN, VERB, ADJECTIVE,
verbs, conjugate, lemma, lexeme, tenses,
predicative, attributive
)
# Import all submodules.
from pattern.text.es import inflect
sys.path.pop(0)
#--- SPANISH PARSER --------------------------------------------------------------------------------
# The Spanish parser (accuracy 92%) is based on the Spanish portion Wikicorpus v.1.0 (FDL license),
# using 1.5M words from the tagged sections 10000-15000.
# Samuel Reese, Gemma Boleda, Montse Cuadros, Lluís Padró, German Rigau.
# Wikicorpus: A Word-Sense Disambiguated Multilingual Wikipedia Corpus.
# Proceedings of 7th Language Resources and Evaluation Conference (LREC'10),
# La Valleta, Malta. May, 2010.
# http://www.lsi.upc.edu/~nlp/wikicorpus/
# The lexicon uses the Parole tagset:
# http://www.lsi.upc.edu/~nlp/SVMTool/parole.html
# http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html
PAROLE = "parole"
parole = {
"AO": "JJ", # primera
"AQ": "JJ", # absurdo
"CC": "CC", # e
"CS": "IN", # porque
"DA": "DT", # el
"DD": "DT", # ese
"DI": "DT", # mucha
"DP": "PRP$", # mi, nuestra
"DT": "DT", # cuántos
"Fa": ".", # !
"Fc": ",", # ,
"Fd": ":", # :
"Fe": "\"", # "
"Fg": ".", # -
"Fh": ".", # /
"Fi": ".", # ?
"Fp": ".", # .
"Fr": ".", # >>
"Fs": ".", # ...
"Fpa": "(", # (
"Fpt": ")", # )
"Fx": ".", # ;
"Fz": ".", #
"I": "UH", # ehm
"NC": "NN", # islam
"NCS": "NN", # guitarra
"NCP": "NNS", # guitarras
"NP": "NNP", # Óscar
"P0": "PRP", # se
"PD": "DT", # ése
"PI": "DT", # uno
"PP": "PRP", # vos
"PR": "WP$", # qué
"PT": "WP$", # qué
"PX": "PRP$", # mío
"RG": "RB", # tecnológicamente
"RN": "RB", # no
"SP": "IN", # por
"VAG": "VBG", # habiendo
"VAI": "MD", # había
"VAN": "MD", # haber
"VAS": "MD", # haya
"VMG": "VBG", # habiendo
"VMI": "VB", # habemos
"VMM": "VB", # compare
"VMN": "VB", # comparecer
"VMP": "VBN", # comparando
"VMS": "VB", # compararan
"VSG": "VBG", # comparando
"VSI": "VB", # será
"VSN": "VB", # ser
"VSP": "VBN", # sido
"VSS": "VB", # sea
"W": "NN", # septiembre
"Z": "CD", # 1,7
"Zd": "CD", # 1,7
"Zm": "CD", # £1,7
"Zp": "CD", # 1,7%
}
def parole2penntreebank(token, tag):
""" Converts a Parole tag to a Penn Treebank II tag.
For example: importantísimo/AQ => importantísimo/ADJ
"""
return (token, parole.get(tag, tag))
def parole2universal(token, tag):
""" Converts a Parole tag to a universal tag.
For example: importantísimo/AQ => importantísimo/ADJ
"""
if tag == "CS":
return (token, CONJ)
if tag == "DP":
return (token, DET)
if tag in ("P0", "PD", "PI", "PP", "PR", "PT", "PX"):
return (token, PRON)
return penntreebank2universal(*parole2penntreebank(token, tag))
ABBREVIATIONS = set((
"a.C.", "a.m.", "apdo.", "aprox.", "Av.", "Avda.", "c.c.", "D.", "Da.", "d.C.",
"d.j.C.", "dna.", "Dr.", "Dra.", "esq.", "etc.", "Gob.", "h.", "m.n.", "no.",
"núm.", "pág.", "P.D.", "P.S.", "p.ej.", "p.m.", "Profa.", "q.e.p.d.", "S.A.",
"S.L.", "Sr.", "Sra.", "Srta.", "s.s.s.", "tel.", "Ud.", "Vd.", "Uds.", "Vds.",
"v.", "vol.", "W.C."
))
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",)):
lemma = singularize(word, pos="DT")
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", {})
return _Parser.find_tokens(self, tokens, **kwargs)
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: parole2penntreebank(token, tag))
if kwargs.get("tagset") == UNIVERSAL:
kwargs.setdefault("map", lambda token, tag: parole2universal(token, tag))
if kwargs.get("tagset") is PAROLE:
kwargs.setdefault("map", lambda token, tag: (token, tag))
return _Parser.find_tags(self, tokens, **kwargs)
parser = Parser(
lexicon = os.path.join(MODULE, "es-lexicon.txt"),
frequency = os.path.join(MODULE, "es-frequency.txt"),
morphology = os.path.join(MODULE, "es-morphology.txt"),
context = os.path.join(MODULE, "es-context.txt"),
default = ("NCS", "NP", "Z"),
language = "es"
)
lexicon = parser.lexicon # Expose lexicon.
spelling = Spelling(
path = os.path.join(MODULE, "es-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)
split = tree # Backwards compatibility.
#---------------------------------------------------------------------------------------------------
# python -m pattern.es xml -s "A quien se hace de miel las moscas le comen." -OTCL
if __name__ == "__main__":
commandline(parse)