312 lines
8.8 KiB
Python
312 lines
8.8 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
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"""This module contains functions and processors used for processing text,
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extracting sentences from text, working with acronyms and abbreviations.
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Data
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----
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.. data:: SEPARATOR - Special separator used in abbreviations.
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.. data:: RE_SENTENCE - Pattern to split text to sentences.
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.. data:: AB_SENIOR - Pattern for detecting abbreviations (example: Sgt. Pepper).
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.. data:: AB_ACRONYM - Pattern for detecting acronyms.
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.. data:: AB_ACRONYM_LETTERS - Pattern for detecting acronyms (example: P.S. I love you).
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.. data:: UNDO_AB_SENIOR - Pattern like AB_SENIOR but with SEPARATOR between abbreviation and next word.
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.. data:: UNDO_AB_ACRONYM - Pattern like AB_ACRONYM but with SEPARATOR between abbreviation and next word.
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"""
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from gensim.summarization.syntactic_unit import SyntacticUnit
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from gensim.parsing.preprocessing import preprocess_documents
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from gensim.utils import tokenize
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from six.moves import xrange
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import re
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import logging
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logger = logging.getLogger('summarizer.preprocessing.cleaner')
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try:
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from pattern.en import tag
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logger.info("'pattern' package found; tag filters are available for English")
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HAS_PATTERN = True
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except ImportError:
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logger.info("'pattern' package not found; tag filters are not available for English")
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HAS_PATTERN = False
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SEPARATOR = r'@'
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RE_SENTENCE = re.compile(r'(\S.+?[.!?])(?=\s+|$)|(\S.+?)(?=[\n]|$)', re.UNICODE)
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AB_SENIOR = re.compile(r'([A-Z][a-z]{1,2}\.)\s(\w)', re.UNICODE)
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AB_ACRONYM = re.compile(r'(\.[a-zA-Z]\.)\s(\w)', re.UNICODE)
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AB_ACRONYM_LETTERS = re.compile(r'([a-zA-Z])\.([a-zA-Z])\.', re.UNICODE)
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UNDO_AB_SENIOR = re.compile(r'([A-Z][a-z]{1,2}\.)' + SEPARATOR + r'(\w)', re.UNICODE)
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UNDO_AB_ACRONYM = re.compile(r'(\.[a-zA-Z]\.)' + SEPARATOR + r'(\w)', re.UNICODE)
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def split_sentences(text):
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"""Split and get list of sentences from given text. It preserves abbreviations set in
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:const:`~gensim.summarization.textcleaner.AB_SENIOR` and :const:`~gensim.summarization.textcleaner.AB_ACRONYM`.
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Parameters
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----------
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text : str
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Input text.
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Returns
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-------
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list of str
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Sentences of given text.
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Example
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-------
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>>> from gensim.summarization.textcleaner import split_sentences
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>>> text = '''Beautiful is better than ugly.
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... Explicit is better than implicit. Simple is better than complex.'''
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>>> split_sentences(text)
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['Beautiful is better than ugly.',
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'Explicit is better than implicit.',
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'Simple is better than complex.']
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"""
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processed = replace_abbreviations(text)
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return [undo_replacement(sentence) for sentence in get_sentences(processed)]
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def replace_abbreviations(text):
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"""Replace blank space to '@' separator after abbreviation and next word.
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Parameters
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----------
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text : str
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Input sentence.
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Returns
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-------
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str
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Sentence with changed separator.
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Example
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-------
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>>> replace_abbreviations("God bless you, please, Mrs. Robinson")
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God bless you, please, Mrs.@Robinson
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"""
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return replace_with_separator(text, SEPARATOR, [AB_SENIOR, AB_ACRONYM])
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def undo_replacement(sentence):
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"""Replace `@` separator back to blank space after each abbreviation.
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Parameters
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----------
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sentence : str
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Input sentence.
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Returns
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-------
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str
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Sentence with changed separator.
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Example
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-------
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>>> undo_replacement("God bless you, please, Mrs.@Robinson")
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God bless you, please, Mrs. Robinson
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"""
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return replace_with_separator(sentence, r" ", [UNDO_AB_SENIOR, UNDO_AB_ACRONYM])
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def replace_with_separator(text, separator, regexs):
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"""Get text with replaced separator if provided regular expressions were matched.
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Parameters
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----------
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text : str
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Input text.
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separator : str
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The separator between words to be replaced.
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regexs : list of `_sre.SRE_Pattern`
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Regular expressions used in processing text.
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Returns
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-------
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str
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Text with replaced separators.
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"""
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replacement = r"\1" + separator + r"\2"
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result = text
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for regex in regexs:
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result = regex.sub(replacement, result)
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return result
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def get_sentences(text):
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"""Sentence generator from provided text. Sentence pattern set
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in :const:`~gensim.summarization.textcleaner.RE_SENTENCE`.
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Parameters
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----------
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text : str
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Input text.
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Yields
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------
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str
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Single sentence extracted from text.
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Example
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-------
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>>> text = "Does this text contains two sentences? Yes, it does."
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>>> for sentence in get_sentences(text):
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>>> print(sentence)
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Does this text contains two sentences?
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Yes, it does.
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"""
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for match in RE_SENTENCE.finditer(text):
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yield match.group()
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def merge_syntactic_units(original_units, filtered_units, tags=None):
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"""Process given sentences and its filtered (tokenized) copies into
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:class:`~gensim.summarization.syntactic_unit.SyntacticUnit`. Also adds tags if they are provided to produced units.
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Parameters
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----------
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original_units : list
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List of original sentences.
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filtered_units : list
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List of tokenized sentences.
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tags : list of str, optional
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List of strings used as tags for each unit. None as deafault.
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Returns
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-------
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list of :class:~gensim.summarization.syntactic_unit.SyntacticUnit
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List of syntactic units (sentences).
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"""
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units = []
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for i in xrange(len(original_units)):
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if filtered_units[i] == '':
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continue
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text = original_units[i]
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token = filtered_units[i]
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tag = tags[i][1] if tags else None
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sentence = SyntacticUnit(text, token, tag)
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sentence.index = i
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units.append(sentence)
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return units
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def join_words(words, separator=" "):
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"""Concatenates `words` with `separator` between elements.
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Parameters
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----------
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words : list of str
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Given words.
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separator : str, optional
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The separator between elements.
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Returns
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-------
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str
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String of merged words with separator between elements.
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"""
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return separator.join(words)
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def clean_text_by_sentences(text):
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"""Tokenize a given text into sentences, applying filters and lemmatize them.
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Parameters
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----------
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text : str
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Given text.
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Returns
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-------
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list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
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Sentences of the given text.
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"""
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original_sentences = split_sentences(text)
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filtered_sentences = [join_words(sentence) for sentence in preprocess_documents(original_sentences)]
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return merge_syntactic_units(original_sentences, filtered_sentences)
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def clean_text_by_word(text, deacc=True):
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"""Tokenize a given text into words, applying filters and lemmatize them.
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Parameters
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----------
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text : str
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Given text.
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deacc : bool, optional
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Remove accentuation if True.
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Returns
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-------
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dict
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Words as keys, :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` as values.
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Example
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-------
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>>> from gensim.summarization.textcleaner import clean_text_by_word
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>>> clean_text_by_word("God helps those who help themselves")
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{'god': Original unit: 'god' *-*-*-* Processed unit: 'god',
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'help': Original unit: 'help' *-*-*-* Processed unit: 'help',
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'helps': Original unit: 'helps' *-*-*-* Processed unit: 'help'}
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"""
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text_without_acronyms = replace_with_separator(text, "", [AB_ACRONYM_LETTERS])
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original_words = list(tokenize(text_without_acronyms, to_lower=True, deacc=deacc))
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filtered_words = [join_words(word_list, "") for word_list in preprocess_documents(original_words)]
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if HAS_PATTERN:
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tags = tag(join_words(original_words)) # tag needs the context of the words in the text
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else:
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tags = None
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units = merge_syntactic_units(original_words, filtered_words, tags)
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return {unit.text: unit for unit in units}
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def tokenize_by_word(text):
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"""Tokenize input text. Before tokenizing transforms text to lower case and removes accentuation and acronyms set
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:const:`~gensim.summarization.textcleaner.AB_ACRONYM_LETTERS`.
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Parameters
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----------
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text : str
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Given text.
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Returns
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-------
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generator
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Generator that yields sequence words of the given text.
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Example
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-------
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>>> from gensim.summarization.textcleaner import tokenize_by_word
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>>> g = tokenize_by_word('Veni. Vedi. Vici.')
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>>> print(next(g))
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veni
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>>> print(next(g))
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vedi
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>>> print(next(g))
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vici
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"""
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text_without_acronyms = replace_with_separator(text, "", [AB_ACRONYM_LETTERS])
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return tokenize(text_without_acronyms, to_lower=True, deacc=True)
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