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| # Natural Language Toolkit: Interface to the Repp Tokenizer | |
| # | |
| # Copyright (C) 2001-2015 NLTK Project | |
| # Authors: Rebecca Dridan and Stephan Oepen | |
| # Contributors: Liling Tan | |
| # | |
| # URL: <https://www.nltk.org/> | |
| # For license information, see LICENSE.TXT | |
| import os | |
| import re | |
| import subprocess | |
| import sys | |
| import tempfile | |
| from nltk.data import ZipFilePathPointer | |
| from nltk.internals import find_dir | |
| from nltk.tokenize.api import TokenizerI | |
| class ReppTokenizer(TokenizerI): | |
| """ | |
| A class for word tokenization using the REPP parser described in | |
| Rebecca Dridan and Stephan Oepen (2012) Tokenization: Returning to a | |
| Long Solved Problem - A Survey, Contrastive Experiment, Recommendations, | |
| and Toolkit. In ACL. http://anthology.aclweb.org/P/P12/P12-2.pdf#page=406 | |
| >>> sents = ['Tokenization is widely regarded as a solved problem due to the high accuracy that rulebased tokenizers achieve.' , | |
| ... 'But rule-based tokenizers are hard to maintain and their rules language specific.' , | |
| ... '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.' | |
| ... ] | |
| >>> tokenizer = ReppTokenizer('/home/alvas/repp/') # doctest: +SKIP | |
| >>> for sent in sents: # doctest: +SKIP | |
| ... tokenizer.tokenize(sent) # doctest: +SKIP | |
| ... | |
| (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'.') | |
| (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'.') | |
| (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'.') | |
| >>> for sent in tokenizer.tokenize_sents(sents): # doctest: +SKIP | |
| ... print(sent) # doctest: +SKIP | |
| ... | |
| (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'.') | |
| (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'.') | |
| (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'.') | |
| >>> for sent in tokenizer.tokenize_sents(sents, keep_token_positions=True): # doctest: +SKIP | |
| ... print(sent) # doctest: +SKIP | |
| ... | |
| [(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)] | |
| [(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)] | |
| [(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)] | |
| """ | |
| def __init__(self, repp_dir, encoding="utf8"): | |
| self.repp_dir = self.find_repptokenizer(repp_dir) | |
| # Set a directory to store the temporary files. | |
| self.working_dir = tempfile.gettempdir() | |
| # Set an encoding for the input strings. | |
| self.encoding = encoding | |
| def tokenize(self, sentence): | |
| """ | |
| Use Repp to tokenize a single sentence. | |
| :param sentence: A single sentence string. | |
| :type sentence: str | |
| :return: A tuple of tokens. | |
| :rtype: tuple(str) | |
| """ | |
| return next(self.tokenize_sents([sentence])) | |
| def tokenize_sents(self, sentences, keep_token_positions=False): | |
| """ | |
| Tokenize multiple sentences using Repp. | |
| :param sentences: A list of sentence strings. | |
| :type sentences: list(str) | |
| :return: A list of tuples of tokens | |
| :rtype: iter(tuple(str)) | |
| """ | |
| with tempfile.NamedTemporaryFile( | |
| prefix="repp_input.", dir=self.working_dir, mode="w", delete=False | |
| ) as input_file: | |
| # Write sentences to temporary input file. | |
| for sent in sentences: | |
| input_file.write(str(sent) + "\n") | |
| input_file.close() | |
| # Generate command to run REPP. | |
| cmd = self.generate_repp_command(input_file.name) | |
| # Decode the stdout and strips the ending newline. | |
| repp_output = self._execute(cmd).decode(self.encoding).strip() | |
| for tokenized_sent in self.parse_repp_outputs(repp_output): | |
| if not keep_token_positions: | |
| # Removes token position information. | |
| tokenized_sent, starts, ends = zip(*tokenized_sent) | |
| yield tokenized_sent | |
| def generate_repp_command(self, inputfilename): | |
| """ | |
| This module generates the REPP command to be used at the terminal. | |
| :param inputfilename: path to the input file | |
| :type inputfilename: str | |
| """ | |
| cmd = [self.repp_dir + "/src/repp"] | |
| cmd += ["-c", self.repp_dir + "/erg/repp.set"] | |
| cmd += ["--format", "triple"] | |
| cmd += [inputfilename] | |
| return cmd | |
| def _execute(cmd): | |
| p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| stdout, stderr = p.communicate() | |
| return stdout | |
| def parse_repp_outputs(repp_output): | |
| """ | |
| This module parses the tri-tuple format that REPP outputs using the | |
| "--format triple" option and returns an generator with tuple of string | |
| tokens. | |
| :param repp_output: | |
| :type repp_output: type | |
| :return: an iterable of the tokenized sentences as tuples of strings | |
| :rtype: iter(tuple) | |
| """ | |
| line_regex = re.compile(r"^\((\d+), (\d+), (.+)\)$", re.MULTILINE) | |
| for section in repp_output.split("\n\n"): | |
| words_with_positions = [ | |
| (token, int(start), int(end)) | |
| for start, end, token in line_regex.findall(section) | |
| ] | |
| words = tuple(t[2] for t in words_with_positions) | |
| yield words_with_positions | |
| def find_repptokenizer(self, repp_dirname): | |
| """ | |
| A module to find REPP tokenizer binary and its *repp.set* config file. | |
| """ | |
| if os.path.exists(repp_dirname): # If a full path is given. | |
| _repp_dir = repp_dirname | |
| else: # Try to find path to REPP directory in environment variables. | |
| _repp_dir = find_dir(repp_dirname, env_vars=("REPP_TOKENIZER",)) | |
| # Checks for the REPP binary and erg/repp.set config file. | |
| assert os.path.exists(_repp_dir + "/src/repp") | |
| assert os.path.exists(_repp_dir + "/erg/repp.set") | |
| return _repp_dir | |