def _get_entity_substring_from_text(self, text, variant, entity_name):
"""
Check ngrams of the text for similarity against the variant (can be a ngram) using Levenshtein distance
and return the closest substring in the text that matches the variant.
For each entity fuziness and min_token_size_for_fuzziness is used from the entity details.
Args:
variant(str or unicode): string, ngram of variant to fuzzy detect in the text using
Levenshtein distance
text(str or unicode): sentence from self.processed on which detection is being done
entity_name (str): name of the entity to get fuzziness and min_token_lenght value
Returns:
str or unicode or None: part of the given text that was detected as entity given the variant,
None otherwise
Example:
>>> text_detector = TextDetector(entity_dict={'city':{})
>>> text = 'Come to Chennai, Tamil Nadu, I will visit Delehi next year'.lower()
>>> text_detector._get_entity_substring_from_text(variant='chennai')
'chennai'
>>> text_detector._get_entity_substring_from_text(variant='delhi')
'delehi'
"""
variant_tokens = TOKENIZER.tokenize(variant)
text_tokens = TOKENIZER.tokenize(text)
original_text_tokens = []
variant_token_i = 0
for text_token in text_tokens:
variant_token = variant_tokens[variant_token_i]
same = variant_token == text_token
# get fuzziness and min_token_size_for_fuziness value from entity dict
entity_dict = self.entities_dict.get(entity_name, {})
# get fuzziness from entity if not set default
fuzziness = entity_dict.get('fuzziness') or self._fuzziness
self.set_fuzziness_low_high_threshold(fuzziness)
min_token_size_for_fuzziness = entity_dict.get(
'min_token_len_fuzziness')
if not min_token_size_for_fuzziness:
min_token_size_for_fuzziness = self._min_token_size_for_fuzziness
ft = self._get_fuzziness_threshold_for_token(token=text_token)
# set substitution cost to one
if same or (len(text_token) > min_token_size_for_fuzziness
and edit_distance(string1=variant_token,
string2=text_token,
substitution_cost=1,
max_distance=ft + 1) <= ft):
original_text_tokens.append(text_token)
variant_token_i += 1
if variant_token_i == len(variant_tokens):
return self._get_substring_from_processed_text(
text, original_text_tokens)
else:
original_text_tokens = []
variant_token_i = 0
return None
def _get_entity_from_text(self, variant, text):
"""
Checks ngrams of the text for similarity against the variant (can be a ngram) using Levenshtein distance
Args:
variant: string, ngram of variant to fuzzy detect in the text using Levenshtein distance
text: text to detect entities from
Returns:
part of the given text that was detected as entity given the variant, None otherwise
Example:
text_detection = TextDetector('city')
...
text_detection._get_entity_from_text(self, variant, text)
text = 'Come to Chennai, Tamil Nadu, I will visit Delehi next year'.lower()
text_detection.get_entity_from_text('chennai', text)
Output:
'chennai'
text_detection.get_entity_from_text('Delhi', text)
Output:
'delehi'
"""
variant_tokens = tokenizer.tokenize(variant.lower())
text_tokens = tokenizer.tokenize(text.lower())
original_text = []
variant_count = 0
for text_token in text_tokens:
variant_token = variant_tokens[variant_count]
utext_token = text_token
if type(utext_token) == 'str':
utext_token = utext_token.decode('utf-8')
same = variant_token == text_token
ft = self._get_fuzziness_threshold_for_token(utext_token)
if same or (len(utext_token) > self._min_token_size_for_fuzziness
and edit_distance(string1=variant_token,
string2=text_token,
max_distance=ft + 1) <= ft):
original_text.append(text_token)
variant_count += 1
if variant_count == len(variant_tokens):
return ' '.join(original_text)
else:
original_text = []
variant_count = 0
return None
def _get_entity_substring_from_text(self, variant, text):
"""
Checks ngrams of the text for similarity against the variant (can be a ngram) using Levenshtein distance
Args:
variant: string, ngram of variant to fuzzy detect in the text using Levenshtein distance
text: text to detect entities from
Returns:
str or unicode: part of the given text that was detected as entity given the variant, None otherwise
Example:
text_detection = TextDetector('city')
...
text_detection._get_entity_from_text(self, variant, text)
text = 'Come to Chennai, Tamil Nadu, I will visit Delehi next year'.lower()
text_detection.get_entity_from_text('chennai', text)
Output:
'chennai'
text_detection.get_entity_from_text('Delhi', text)
Output:
'delehi'
"""
variant_tokens = TOKENIZER.tokenize(variant)
text_tokens = TOKENIZER.tokenize(text)
original_text_tokens = []
variant_token_i = 0
for text_token in text_tokens:
variant_token = variant_tokens[variant_token_i]
same = variant_token == text_token
ft = self._get_fuzziness_threshold_for_token(text_token)
if same or (len(text_token) > self._min_token_size_for_fuzziness
and edit_distance(string1=variant_token,
string2=text_token,
max_distance=ft + 1) <= ft):
original_text_tokens.append(text_token)
variant_token_i += 1
if variant_token_i == len(variant_tokens):
return self._get_substring_from_processed_text(
original_text_tokens)
else:
original_text_tokens = []
variant_token_i = 0
return None
def _get_entity_substring_from_text(self, text, variant):
"""
Check ngrams of the text for similarity against the variant (can be a ngram) using Levenshtein distance
and return the closest substring in the text that matches the variant
Args:
variant(str or unicode): string, ngram of variant to fuzzy detect in the text using Levenshtein distance
text(str or unicode): sentence from self.processed on which detection is being done
Returns:
str or unicode or None: part of the given text that was detected as entity given the variant,
None otherwise
Example:
>>> text_detector = TextDetector('city')
>>> text = 'Come to Chennai, Tamil Nadu, I will visit Delehi next year'.lower()
>>> text_detector.detect_entity(text)
>>> text_detector._get_entity_substring_from_text(variant='chennai')
'chennai'
>>> text_detector._get_entity_substring_from_text(variant='delhi')
'delehi'
"""
variant_tokens = TOKENIZER.tokenize(variant)
text_tokens = TOKENIZER.tokenize(text)
original_text_tokens = []
variant_token_i = 0
for text_token in text_tokens:
variant_token = variant_tokens[variant_token_i]
same = variant_token == text_token
ft = self._get_fuzziness_threshold_for_token(text_token)
if same or (len(text_token) > self._min_token_size_for_fuzziness
and edit_distance(string1=variant_token,
string2=text_token,
max_distance=ft + 1) <= ft):
original_text_tokens.append(text_token)
variant_token_i += 1
if variant_token_i == len(variant_tokens):
return self._get_substring_from_processed_text(
text, original_text_tokens)
else:
original_text_tokens = []
variant_token_i = 0
return None