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267 lines
8.1 KiB
Python
267 lines
8.1 KiB
Python
#### PATTERN | IT ##################################################################################
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# -*- coding: utf-8 -*-
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# Copyright (c) 2013 University of Antwerp, Belgium
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# Copyright (c) 2013 St. Lucas University College of Art & Design, Antwerp.
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# Author: Tom De Smedt <tom@organisms.be>, Fabio Marfia <marfia@elet.polimi.it>
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# License: BSD (see LICENSE.txt for details).
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####################################################################################################
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# Italian linguistical tools using fast regular expressions.
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from __future__ import unicode_literals
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from __future__ import division
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from builtins import str, bytes, dict, int
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from builtins import map, zip, filter
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from builtins import object, range
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import os
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import sys
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try:
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MODULE = os.path.dirname(os.path.realpath(__file__))
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except:
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MODULE = ""
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sys.path.insert(0, os.path.join(MODULE, "..", "..", "..", ".."))
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# Import parser base classes.
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from pattern.text import (
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Lexicon, Model, Morphology, Context, Parser as _Parser, ngrams, pprint, commandline,
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PUNCTUATION
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)
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# Import parser universal tagset.
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from pattern.text import (
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penntreebank2universal as _penntreebank2universal,
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PTB, PENN, UNIVERSAL,
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NOUN, VERB, ADJ, ADV, PRON, DET, PREP, ADP, NUM, CONJ, INTJ, PRT, PUNC, X
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)
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# Import parse tree base classes.
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from pattern.text.tree import (
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Tree, Text, Sentence, Slice, Chunk, PNPChunk, Chink, Word, table,
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SLASH, WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA, AND, OR
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)
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# Import sentiment analysis base classes.
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from pattern.text import (
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Sentiment as _Sentiment,
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NOUN, VERB, ADJECTIVE, ADVERB,
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MOOD, IRONY
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)
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# Import spelling base class.
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from pattern.text import (
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Spelling
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)
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# Import verb tenses.
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from pattern.text import (
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INFINITIVE, PRESENT, PAST, FUTURE, CONDITIONAL,
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FIRST, SECOND, THIRD,
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SINGULAR, PLURAL, SG, PL,
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INDICATIVE, IMPERATIVE, SUBJUNCTIVE,
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IMPERFECTIVE, PERFECTIVE, PROGRESSIVE,
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IMPERFECT, PRETERITE,
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PARTICIPLE, GERUND
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)
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# Import inflection functions.
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from pattern.text.it.inflect import (
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article, referenced, DEFINITE, INDEFINITE,
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pluralize, singularize, NOUN, VERB, ADJECTIVE,
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verbs, conjugate, lemma, lexeme, tenses,
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predicative, attributive,
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gender, MASCULINE, MALE, FEMININE, FEMALE, NEUTER, NEUTRAL, PLURAL, M, F, N, PL
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)
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# Import all submodules.
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from pattern.text.it import inflect
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sys.path.pop(0)
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#--- PARSER ----------------------------------------------------------------------------------------
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_subordinating_conjunctions = set((
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"che" , "perché", "sebbene",
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"come" , "poiché", "senza",
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"se" , "perciò", "salvo",
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"mentre", "finché", "dopo",
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"quando", "benché"
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))
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def penntreebank2universal(token, tag):
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""" Converts a Penn Treebank II tag to a universal tag.
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For example: che/IN => che/CONJ
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"""
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if tag == "IN" and token.lower() in _subordinating_conjunctions:
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return CONJ
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return _penntreebank2universal(token, tag)
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ABBREVIATIONS = [
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"a.C.", "all.", "apr.", "art.", "artt.", "b.c.", "c.a.", "cfr.", "c.d.",
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"c.m.", "C.V.", "d.C.", "Dott.", "ecc.", "egr.", "e.v.", "fam.", "giu.",
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"Ing.", "L.", "n.", "op.", "orch.", "p.es.", "Prof.", "prof.", "ql.co.",
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"secc.", "sig.", "s.l.m.", "s.r.l.", "Spett.", "S.P.Q.C.", "v.c."
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]
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replacements = (
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"a", "co", "all", "anch", "nient", "cinquant",
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"b", "de", "dev", "bell", "quell", "diciott",
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"c", "gl", "don", "cent", "quest", "occupo",
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"d", "po", "dov", "dall", "trent", "sessant",
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"l", "un", "nel", "dell", "tropp",
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"m", "king",
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"n", "nell",
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"r", "sant",
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"s", "sott",
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"sull",
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"tant",
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"tutt",
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"vent")
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replacements += tuple(k.capitalize() for k in replacements)
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replacements = dict((k + "'", k + "' ") for k in replacements)
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def find_lemmata(tokens):
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""" Annotates the tokens with lemmata for plural nouns and conjugated verbs,
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where each token is a [word, part-of-speech] list.
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"""
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for token in tokens:
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word, pos, lemma = token[0], token[1], token[0]
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if pos.startswith(("DT",)):
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lemma = singularize(word, pos="DT")
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if pos.startswith("JJ"):
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lemma = predicative(word)
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if pos == "NNS":
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lemma = singularize(word)
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if pos.startswith(("VB", "MD")):
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lemma = conjugate(word, INFINITIVE) or word
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token.append(lemma.lower())
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return tokens
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class Parser(_Parser):
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def find_tokens(self, tokens, **kwargs):
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kwargs.setdefault("abbreviations", ABBREVIATIONS)
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kwargs.setdefault("replace", replacements)
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#return _Parser.find_tokens(self, tokens, **kwargs)
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s = _Parser.find_tokens(self, tokens, **kwargs)
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s = [s.replace(" &contraction ;", "'").replace("XXX -", "-") for s in s]
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return s
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def find_lemmata(self, tokens, **kwargs):
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return find_lemmata(tokens)
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def find_tags(self, tokens, **kwargs):
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if kwargs.get("tagset") in (PENN, None):
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kwargs.setdefault("map", lambda token, tag: (token, tag))
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if kwargs.get("tagset") == UNIVERSAL:
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kwargs.setdefault("map", lambda token, tag: penntreebank2universal(token, tag))
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return _Parser.find_tags(self, tokens, **kwargs)
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class Sentiment(_Sentiment):
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def load(self, path=None):
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_Sentiment.load(self, path)
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parser = Parser(
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lexicon = os.path.join(MODULE, "it-lexicon.txt"),
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frequency = os.path.join(MODULE, "it-frequency.txt"),
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morphology = os.path.join(MODULE, "it-morphology.txt"),
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context = os.path.join(MODULE, "it-context.txt"),
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default = ("NN", "NNP", "CD"),
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language = "it"
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)
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lexicon = parser.lexicon # Expose lexicon.
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sentiment = Sentiment(
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path = os.path.join(MODULE, "it-sentiment.xml"),
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synset = None,
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negations = ("mai", "no", "non"),
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modifiers = ("RB",),
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modifier = lambda w: w.endswith(("mente")),
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tokenizer = parser.find_tokens,
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language = "it"
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)
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spelling = Spelling(
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path = os.path.join(MODULE, "it-spelling.txt")
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)
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def tokenize(s, *args, **kwargs):
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""" Returns a list of sentences, where punctuation marks have been split from words.
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"""
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return parser.find_tokens(s, *args, **kwargs)
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def parse(s, *args, **kwargs):
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""" Returns a tagged Unicode string.
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"""
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return parser.parse(s, *args, **kwargs)
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def parsetree(s, *args, **kwargs):
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""" Returns a parsed Text from the given string.
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"""
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return Text(parse(s, *args, **kwargs))
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def tree(s, token=[WORD, POS, CHUNK, PNP, REL, LEMMA]):
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""" Returns a parsed Text from the given parsed string.
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"""
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return Text(s, token)
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def tag(s, tokenize=True, encoding="utf-8", **kwargs):
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""" Returns a list of (token, tag)-tuples from the given string.
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"""
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tags = []
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for sentence in parse(s, tokenize, True, False, False, False, encoding, **kwargs).split():
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for token in sentence:
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tags.append((token[0], token[1]))
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return tags
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def keywords(s, top=10, **kwargs):
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""" Returns a sorted list of keywords in the given string.
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"""
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return parser.find_keywords(s, **dict({
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"frequency": parser.frequency,
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"top": top,
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"pos": ("NN",),
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"ignore": ("rt",)}, **kwargs))
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def suggest(w):
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""" Returns a list of (word, confidence)-tuples of spelling corrections.
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"""
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return spelling.suggest(w)
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def polarity(s, **kwargs):
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""" Returns the sentence polarity (positive/negative) between -1.0 and 1.0.
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"""
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return sentiment(s, **kwargs)[0]
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def subjectivity(s, **kwargs):
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""" Returns the sentence subjectivity (objective/subjective) between 0.0 and 1.0.
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"""
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return sentiment(s, **kwargs)[1]
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def positive(s, threshold=0.1, **kwargs):
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""" Returns True if the given sentence has a positive sentiment (polarity >= threshold).
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"""
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return polarity(s, **kwargs) >= threshold
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split = tree # Backwards compatibility.
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#---------------------------------------------------------------------------------------------------
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# python -m pattern.it xml -s "Il gatto nero faceva le fusa." -OTCL
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if __name__ == "__main__":
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commandline(parse)
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