152 lines
8.1 KiB
Python
152 lines
8.1 KiB
Python
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# -*- coding: utf-8 -*-
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# Natural Language Toolkit: Interface to the Repp Tokenizer
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#
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# Copyright (C) 2001-2015 NLTK Project
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# Authors: Rebecca Dridan and Stephan Oepen
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# Contributors: Liling Tan
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#
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import unicode_literals, print_function
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from six import text_type
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import os
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import re
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import sys
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import subprocess
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import tempfile
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from nltk.data import ZipFilePathPointer
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from nltk.internals import find_dir
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from nltk.tokenize.api import TokenizerI
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class ReppTokenizer(TokenizerI):
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"""
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A class for word tokenization using the REPP parser described in
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Rebecca Dridan and Stephan Oepen (2012) Tokenization: Returning to a
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Long Solved Problem - A Survey, Contrastive Experiment, Recommendations,
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and Toolkit. In ACL. http://anthology.aclweb.org/P/P12/P12-2.pdf#page=406
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>>> sents = ['Tokenization is widely regarded as a solved problem due to the high accuracy that rulebased tokenizers achieve.' ,
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... 'But rule-based tokenizers are hard to maintain and their rules language specific.' ,
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... 'We evaluated our method on three languages and obtained error rates of 0.27% (English), 0.35% (Dutch) and 0.76% (Italian) for our best models.'
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... ]
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>>> tokenizer = ReppTokenizer('/home/alvas/repp/') # doctest: +SKIP
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>>> for sent in sents: # doctest: +SKIP
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... tokenizer.tokenize(sent) # doctest: +SKIP
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...
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(u'Tokenization', u'is', u'widely', u'regarded', u'as', u'a', u'solved', u'problem', u'due', u'to', u'the', u'high', u'accuracy', u'that', u'rulebased', u'tokenizers', u'achieve', u'.')
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(u'But', u'rule-based', u'tokenizers', u'are', u'hard', u'to', u'maintain', u'and', u'their', u'rules', u'language', u'specific', u'.')
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(u'We', u'evaluated', u'our', u'method', u'on', u'three', u'languages', u'and', u'obtained', u'error', u'rates', u'of', u'0.27', u'%', u'(', u'English', u')', u',', u'0.35', u'%', u'(', u'Dutch', u')', u'and', u'0.76', u'%', u'(', u'Italian', u')', u'for', u'our', u'best', u'models', u'.')
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>>> for sent in tokenizer.tokenize_sents(sents): # doctest: +SKIP
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... print sent # doctest: +SKIP
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...
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(u'Tokenization', u'is', u'widely', u'regarded', u'as', u'a', u'solved', u'problem', u'due', u'to', u'the', u'high', u'accuracy', u'that', u'rulebased', u'tokenizers', u'achieve', u'.')
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(u'But', u'rule-based', u'tokenizers', u'are', u'hard', u'to', u'maintain', u'and', u'their', u'rules', u'language', u'specific', u'.')
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(u'We', u'evaluated', u'our', u'method', u'on', u'three', u'languages', u'and', u'obtained', u'error', u'rates', u'of', u'0.27', u'%', u'(', u'English', u')', u',', u'0.35', u'%', u'(', u'Dutch', u')', u'and', u'0.76', u'%', u'(', u'Italian', u')', u'for', u'our', u'best', u'models', u'.')
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>>> for sent in tokenizer.tokenize_sents(sents, keep_token_positions=True): # doctest: +SKIP
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... print sent # doctest: +SKIP
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...
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[(u'Tokenization', 0, 12), (u'is', 13, 15), (u'widely', 16, 22), (u'regarded', 23, 31), (u'as', 32, 34), (u'a', 35, 36), (u'solved', 37, 43), (u'problem', 44, 51), (u'due', 52, 55), (u'to', 56, 58), (u'the', 59, 62), (u'high', 63, 67), (u'accuracy', 68, 76), (u'that', 77, 81), (u'rulebased', 82, 91), (u'tokenizers', 92, 102), (u'achieve', 103, 110), (u'.', 110, 111)]
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[(u'But', 0, 3), (u'rule-based', 4, 14), (u'tokenizers', 15, 25), (u'are', 26, 29), (u'hard', 30, 34), (u'to', 35, 37), (u'maintain', 38, 46), (u'and', 47, 50), (u'their', 51, 56), (u'rules', 57, 62), (u'language', 63, 71), (u'specific', 72, 80), (u'.', 80, 81)]
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[(u'We', 0, 2), (u'evaluated', 3, 12), (u'our', 13, 16), (u'method', 17, 23), (u'on', 24, 26), (u'three', 27, 32), (u'languages', 33, 42), (u'and', 43, 46), (u'obtained', 47, 55), (u'error', 56, 61), (u'rates', 62, 67), (u'of', 68, 70), (u'0.27', 71, 75), (u'%', 75, 76), (u'(', 77, 78), (u'English', 78, 85), (u')', 85, 86), (u',', 86, 87), (u'0.35', 88, 92), (u'%', 92, 93), (u'(', 94, 95), (u'Dutch', 95, 100), (u')', 100, 101), (u'and', 102, 105), (u'0.76', 106, 110), (u'%', 110, 111), (u'(', 112, 113), (u'Italian', 113, 120), (u')', 120, 121), (u'for', 122, 125), (u'our', 126, 129), (u'best', 130, 134), (u'models', 135, 141), (u'.', 141, 142)]
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"""
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def __init__(self, repp_dir, encoding='utf8'):
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self.repp_dir = self.find_repptokenizer(repp_dir)
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# Set a directory to store the temporary files.
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self.working_dir = tempfile.gettempdir()
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# Set an encoding for the input strings.
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self.encoding = encoding
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def tokenize(self, sentence):
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"""
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Use Repp to tokenize a single sentence.
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:param sentence: A single sentence string.
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:type sentence: str
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:return: A tuple of tokens.
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:rtype: tuple(str)
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"""
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return next(self.tokenize_sents([sentence]))
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def tokenize_sents(self, sentences, keep_token_positions=False):
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"""
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Tokenize multiple sentences using Repp.
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:param sentences: A list of sentence strings.
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:type sentences: list(str)
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:return: A list of tuples of tokens
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:rtype: iter(tuple(str))
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"""
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with tempfile.NamedTemporaryFile(prefix='repp_input.',
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dir=self.working_dir, mode='w', delete=False) as input_file:
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# Write sentences to temporary input file.
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for sent in sentences:
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input_file.write(text_type(sent) + '\n')
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input_file.close()
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# Generate command to run REPP.
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cmd =self.generate_repp_command(input_file.name)
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# Decode the stdout and strips the ending newline.
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repp_output = self._execute(cmd).decode(self.encoding).strip()
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for tokenized_sent in self.parse_repp_outputs(repp_output):
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if not keep_token_positions:
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# Removes token position information.
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tokenized_sent, starts, ends = zip(*tokenized_sent)
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yield tokenized_sent
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def generate_repp_command(self, inputfilename):
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"""
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This module generates the REPP command to be used at the terminal.
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:param inputfilename: path to the input file
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:type inputfilename: str
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"""
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cmd = [self.repp_dir + '/src/repp']
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cmd+= ['-c', self.repp_dir + '/erg/repp.set']
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cmd+= ['--format', 'triple']
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cmd+= [inputfilename]
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return cmd
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@staticmethod
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def _execute(cmd):
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p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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stdout, stderr = p.communicate()
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return stdout
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@staticmethod
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def parse_repp_outputs(repp_output):
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"""
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This module parses the tri-tuple format that REPP outputs using the
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"--format triple" option and returns an generator with tuple of string
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tokens.
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:param repp_output:
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:type repp_output: type
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:return: an iterable of the tokenized sentences as tuples of strings
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:rtype: iter(tuple)
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"""
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line_regex = re.compile('^\((\d+), (\d+), (.+)\)$', re.MULTILINE)
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for section in repp_output.split('\n\n'):
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words_with_positions = [(token, int(start), int(end))
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for start, end, token in
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line_regex.findall(section)]
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words = tuple(t[2] for t in words_with_positions)
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yield words_with_positions
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def find_repptokenizer(self, repp_dirname):
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"""
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A module to find REPP tokenizer binary and its *repp.set* config file.
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"""
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if os.path.exists(repp_dirname): # If a full path is given.
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_repp_dir = repp_dirname
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else: # Try to find path to REPP directory in environment variables.
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_repp_dir = find_dir(repp_dirname, env_vars=('REPP_TOKENIZER',))
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# Checks for the REPP binary and erg/repp.set config file.
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assert os.path.exists(_repp_dir+'/src/repp')
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assert os.path.exists(_repp_dir+'/erg/repp.set')
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return _repp_dir
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