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234 lines
7.2 KiB
Python
234 lines
7.2 KiB
Python
#### PATTERN | EN ##################################################################################
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# -*- coding: utf-8 -*-
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# Copyright (c) 2010 University of Antwerp, Belgium
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# Author: Tom De Smedt <tom@organisms.be>
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# License: BSD (see LICENSE.txt for details).
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# http://www.clips.ua.ac.be/pages/pattern
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####################################################################################################
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# English 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,
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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, NOUN, VERB, ADJECTIVE, ADVERB
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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,
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FIRST, SECOND, THIRD,
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SINGULAR, PLURAL, SG, PL,
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PROGRESSIVE,
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PARTICIPLE
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)
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# Import inflection functions.
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from pattern.text.en.inflect import (
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article, referenced, DEFINITE, INDEFINITE,
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pluralize, singularize, NOUN, VERB, ADJECTIVE,
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grade, comparative, superlative, COMPARATIVE, SUPERLATIVE,
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verbs, conjugate, lemma, lexeme, tenses,
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predicative, attributive
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)
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# Import quantification functions.
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from pattern.text.en.inflect_quantify import (
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number, numerals, quantify, reflect
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)
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# Import mood & modality functions.
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from pattern.text.en.modality import (
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mood, INDICATIVE, IMPERATIVE, CONDITIONAL, SUBJUNCTIVE,
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modality, uncertain, EPISTEMIC,
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negated
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)
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# Import all submodules.
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from pattern.text.en import inflect
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from pattern.text.en import wordnet
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from pattern.text.en import wordlist
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sys.path.pop(0)
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#--- ENGLISH PARSER --------------------------------------------------------------------------------
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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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# cats => cat
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if pos == "NNS":
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lemma = singularize(word)
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# sat => sit
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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_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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# Map "terrible" to adverb "terribly" (+1% accuracy)
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if not path:
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for w, pos in list(dict.items(self)):
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if "JJ" in pos:
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if w.endswith("y"):
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w = w[:-1] + "i"
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if w.endswith("le"):
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w = w[:-2]
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p, s, i = pos["JJ"]
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self.annotate(w + "ly", "RB", p, s, i)
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parser = Parser(
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lexicon = os.path.join(MODULE, "en-lexicon.txt"), # A dict of known words => most frequent tag.
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frequency = os.path.join(MODULE, "en-frequency.txt"), # A dict of word frequency.
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model = os.path.join(MODULE, "en-model.slp"), # A SLP classifier trained on WSJ (01-07).
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morphology = os.path.join(MODULE, "en-morphology.txt"), # A set of suffix rules (e.g., -ly = adverb).
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context = os.path.join(MODULE, "en-context.txt"), # A set of contextual rules.
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entities = os.path.join(MODULE, "en-entities.txt"), # A dict of named entities: John = NNP-PERS.
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default = ("NN", "NNP", "CD"),
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language = "en"
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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, "en-sentiment.xml"),
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synset = "wordnet_id",
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negations = ("no", "not", "n't", "never"),
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modifiers = ("RB",),
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modifier = lambda w: w.endswith("ly"),
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tokenizer = parser.find_tokens,
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language = "en"
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)
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spelling = Spelling(
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path=os.path.join(MODULE, "en-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.en xml -s "The cat sat on the mat." -OTCL
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if __name__ == "__main__":
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commandline(parse)
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