# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import sys
import unicodedata
from deeppavlov.core.common.registry import register
from deeppavlov.core.models.component import Component
[docs]@register('sanitizer')
class Sanitizer(Component):
"""Remove all combining characters like diacritical marks from tokens
Args:
diacritical: whether to remove diacritical signs or not
diacritical signs are something like hats and stress marks
nums: whether to replace all digits with 1 or not
"""
def __init__(self,
diacritical: bool = True,
nums: bool = False,
*args, **kwargs) -> None:
self.diacritical = diacritical
self.nums = nums
self.combining_characters = dict.fromkeys([c for c in range(sys.maxunicode)
if unicodedata.combining(chr(c))])
def filter_diacritical(self, tokens_batch):
"""Takes batch of tokens and returns the batch with sanitized tokens"""
sanitized_batch = []
for utterance in tokens_batch:
sanitized_utterance = []
for token in utterance:
token = unicodedata.normalize('NFD', token)
sanitized_utterance.append(token.translate(self.combining_characters))
sanitized_batch.append(sanitized_utterance)
return sanitized_batch
def replace_nums(self, tokens_batch):
sanitized_batch = []
for utterance in tokens_batch:
sanitized_batch.append([re.sub('[0-9]', '1', token) for token in utterance])
return sanitized_batch
def __call__(self, tokens_batch, **kwargs):
if self.filter_diacritical:
tokens_batch = self.filter_diacritical(tokens_batch)
if self.nums:
tokens_batch = self.replace_nums(tokens_batch)
return tokens_batch