def remove_key_words(text: str, offsets: List[Offset],
rate: int) -> Tuple[str, List[Offset]]:
"""
Modify text to remove some key words, making the learning harder and the model more robust.
:param text: original paragraph as a string
:param offsets: list of extracted offsets
:param rate: chance as an integer between 1 and 100 that a key word is removed
:return: a tuple (new_text, offsets)
"""
words_to_delete_offsets: List[Offset] = key_words_matcher.get_matches(
text=text, tag="TO_DELETE")
if (len(words_to_delete_offsets) == 0) or (len(offsets) == 0):
return text, offsets
detected_spans = dict()
for offset in offsets:
span_text = text[offset.start:offset.end]
if len(span_text) > 0:
detected_spans[span_text] = offset.type
if len(detected_spans) == 0:
return text, offsets
original_content_offsets_matcher = AcoraMatcher(content=list(
detected_spans.keys()),
ignore_case=False)
cleaned_text = list()
start_selection_offset = 0
for offset in words_to_delete_offsets:
if randint(1, 99) < rate:
# - 1 to remove also the space following the keyword to remove
cleaned_text.append(text[start_selection_offset:offset.start - 1])
start_selection_offset = offset.end
else:
cleaned_text.append(text[start_selection_offset:offset.end])
start_selection_offset = offset.end
cleaned_text.append(text[start_selection_offset:len(text)])
cleaned_text = ''.join(cleaned_text)
updated_offsets = original_content_offsets_matcher.get_matches(
text=cleaned_text, tag="UNKNOWN")
offsets_to_return = list()
# restore original offset type name
for offset in updated_offsets:
span_text = cleaned_text[offset.start:offset.end]
type_name = detected_spans[span_text]
offsets_to_return.append(Offset(offset.start, offset.end, type_name))
return cleaned_text, offsets_to_return
def complete_case_annotations(spacy_docs: List[Doc],
entity_typename: Dict[str, str]) -> List[Doc]:
"""
Complete/Normalize annotations from the spacy tagger.
:param spacy_docs: the spacy annotations
:param entity_typename: the dictionary with each occurence type
:returns: the updated spacy_annotions (for convenience only, as the update is inplace)
"""
if len(spacy_docs) > 0:
matcher = AcoraMatcher(content=list(entity_typename.keys()),
ignore_case=True)
doc_text, empty_offsets = zip(*[(spacy_doc.text, [])
for spacy_doc in spacy_docs])
document_addresses_offsets = find_address_in_block_of_paragraphs(
texts=list(doc_text), offsets=list(empty_offsets))
for spacy_doc, doc_address_offset in zip(spacy_docs,
document_addresses_offsets):
matches = matcher.get_matches(text=spacy_doc.text, tag="UNKNOWN")
matcher_offsets = list()
for offset in matches:
span_text = spacy_doc.text[offset.start:offset.end]
logger.debug(span_text)
offset.type = entity_typename[span_text.lower()]
matcher_offsets.append(offset)
matcher_offsets_normalized = normalize_offsets(
offsets=matcher_offsets + doc_address_offset)
spacy_matcher_offset: List[Span] = list()
for offset in matcher_offsets_normalized:
# https://spacy.io/usage/linguistic-features#section-named-entities
span_doc: Span = spacy_doc.char_span(offset.start,
offset.end,
label=offset.type)
if span_doc is not None:
# span will be none if the word is incomplete
spacy_matcher_offset.append(span_doc)
else:
logger.error(
f"ERROR char offset [{spacy_doc.text[offset.start:offset.end]}] "
f"from [{spacy_doc.text}]")
spacy_doc.ents = spacy_matcher_offset # all_offsets
return spacy_docs
class CourtName:
court_names = set()
matcher = None
def __init__(self):
"""
Build a matcher of French court names based on a list available in open data
https://www.data.gouv.fr/fr/datasets/les-statistiques-par-juridiction/#_
(the list has more data, the one store is an extraction)
"""
config = get_config_default()
file = config["french_court_names"]
with open(file) as f1:
for line in f1.readlines():
clean_text = line.strip()
if len(clean_text) > 0:
self.court_names.add(clean_text)
assert len(self.court_names) > 1000
self.matcher = AcoraMatcher(content=list(self.court_names),
ignore_case=True)
def get_matches(self, text: str) -> List[Offset]:
"""
Find match of French court names in a text
:param text: original text
:return: list of offsets
"""
return self.matcher.get_matches(text=text, tag="COURT")
class PostalCodeCity:
matcher = None
def __init__(self):
"""
Build a matcher of first name based on a French names dictionary
"""
postal_code_city_list = list()
config = get_config_default()
file = config["postal_code_city"]
with open(file) as f1:
for line in f1.readlines():
fields = line.split(";")
city = fields[1].strip()
if len(city) >= 3:
postal_code = fields[2].strip()
postal_code_city_list.append(postal_code + " " + city)
postal_code_city_list.append(city + " (" + postal_code +
")")
assert len(postal_code_city_list) > 1000
postal_code_city_list.pop(0)
self.matcher = AcoraMatcher(list(postal_code_city_list),
ignore_case=True)
def get_matches(self, text: str) -> List[Offset]:
"""
Find match of postal code and city names in a text
:param text: original text
:return: list of offsets
"""
return self.matcher.get_matches(text=text, tag="ADDRESS")
def __init__(self):
"""
Build a matcher of French court names based on a list available in open data
https://www.data.gouv.fr/fr/datasets/les-statistiques-par-juridiction/#_
(the list has more data, the one store is an extraction)
"""
config = get_config_default()
file = config["french_court_names"]
with open(file) as f1:
for line in f1.readlines():
clean_text = line.strip()
if len(clean_text) > 0:
self.court_names.add(clean_text)
assert len(self.court_names) > 1000
self.matcher = AcoraMatcher(content=list(self.court_names),
ignore_case=True)
def __init__(self, path_trainset: str, threshold_occurrences: int,
type_name_to_not_load: List[str], load_data: bool):
"""
Build an Acora matcher based on the dict of frequent entities
:param path_trainset: path to a file storing the entity
:param threshold_occurrences: minimum number of occurences of the entity
:param type_name_to_not_load: type of entities that should not be loaded to avoid fake match
:param load_data: boolean to decide if data should be loaded or not
:return: an Acora matcher matcher
"""
if load_data:
self.frequent_entities_dict = self.__read_frequent_entities(
path_trainset=path_trainset,
threshold_occurrences=threshold_occurrences,
type_name_to_not_load=type_name_to_not_load)
self.matcher = AcoraMatcher(content=list(
self.frequent_entities_dict.keys()),
ignore_case=True)
else:
self.matcher = AcoraMatcher(content=["!@#$%^&*()"],
ignore_case=True)
def __init__(self):
"""
Build a matcher of first name based on a French names dictionary
"""
postal_code_city_list = list()
config = get_config_default()
file = config["postal_code_city"]
with open(file) as f1:
for line in f1.readlines():
fields = line.split(";")
city = fields[1].strip()
if len(city) >= 3:
postal_code = fields[2].strip()
postal_code_city_list.append(postal_code + " " + city)
postal_code_city_list.append(city + " (" + postal_code +
")")
assert len(postal_code_city_list) > 1000
postal_code_city_list.pop(0)
self.matcher = AcoraMatcher(list(postal_code_city_list),
ignore_case=True)
def get_matcher_of_clerks_from_headers(self) -> AcoraMatcher:
"""
Create variations of items to search
:return: a matcher of string which ignore case
"""
header_content = self.current_header['greffier']
matcher = AcoraMatcher(content=header_content, ignore_case=False)
# for content in header_content:
# first_name, last_name = get_first_last_name(content)
# if len(first_name) > self.threshold_size:
# matcher.add(first_name)
# if len(last_name) > self.threshold_size:
# matcher.add(last_name)
return matcher
def __init__(self, ignore_case: bool):
"""
Build a matcher of first name based on a French names dictionary
:type ignore_case: True to ignore case during matching
:return: Acora matcher
"""
config = get_config_default()
file1 = config["first_name_dict_1"]
file2 = config["first_name_dict_2"]
firs_name = set()
with open(file1) as f1:
for line in f1.readlines():
fields = line.split(";")
# all names start with a Upcase letter and finishes with a space
text = fields[3].strip()
if len(text) >= 4:
firs_name.add(text)
with open(file2, encoding="ISO-8859-1") as f2:
for line in f2.readlines():
fields = line.split(";")
text = fields[0].strip()
if len(text) >= 4:
firs_name.add(get_title_case(text))
to_remove = [
"Elle", "France", "Mercedes", "Paris", "Alger", "Oran", "Sans"
]
for item_to_remove in to_remove:
firs_name.remove(item_to_remove)
self.first_name_dict = firs_name
self.matcher = AcoraMatcher(content=list(self.first_name_dict),
ignore_case=ignore_case)
def get_matcher_of_partie_pm_from_headers(self) -> AcoraMatcher:
"""
Create variations of items to search
:return: a matcher of string which ignore case
"""
span_text = list()
for full_content, short_content in zip(
self.current_header['defendeur_fullname'] + self.current_header['demandeur_fullname'],
self.current_header['defendeur_hidden'] + self.current_header['demandeur_hidden']):
if short_content is None:
span_text.append(full_content)
matcher = AcoraMatcher(content=span_text, ignore_case=False)
return matcher
def test_builder(cls, content: Dict[str, str]):
"""
Build an instance of this object for tests.
In particular, don't try to read saved data
:param content: a dictionary of entities to load
:return: an instance of FrequentEntities class
"""
instance = FrequentEntities(path_trainset="",
threshold_occurrences=0,
load_data=False,
type_name_to_not_load=[])
instance.frequent_entities_dict = content
instance.matcher = AcoraMatcher(content=list(content.keys()),
ignore_case=True)
return instance
def get_matcher_of_partie_pp_from_headers(self) -> AcoraMatcher:
"""
Create variations of items to search
:return: a matcher of string which ignore case
"""
# this way of init assure that the matcher doesn't expect binary data
# this may happen if we load empty arrays through update function for instance
span_text = list()
for full_content, short_content in zip(
self.current_header['defendeur_fullname'] + self.current_header['demandeur_fullname'],
self.current_header['defendeur_hidden'] + self.current_header['demandeur_hidden']):
if short_content is not None:
span_text.append(full_content)
# first_name, last_name = get_first_last_name(full_content)
# if len(first_name) > self.threshold_size:
# matcher.add(first_name)
# if len(last_name) > self.threshold_size:
# matcher.add(last_name)
matcher = AcoraMatcher(content=span_text, ignore_case=False)
return matcher
def get_all_name_variation(texts: List[str], offsets: List[List[Offset]], threshold_span_size: int) -> List[List[Offset]]:
"""
Search for any variation of known entities
:param texts: original text
:param offsets: discovered offsets
:param threshold_span_size: minimum size of a name (first / last) to be added to the list
:return: discovered offsets
"""
pp_text_span = list()
pm_text_span = list()
for current_offsets, text in zip(offsets, texts):
for offset in current_offsets:
# start_offset, end_offset, type_name = offset
text_span = text[offset.start:offset.end].strip()
if len(text_span) > 0:
if offset.type == "PERS":
pp_text_span.append(text_span)
first_name, last_name = get_first_last_name(text_span)
first_name = first_name.strip()
last_name = last_name.strip()
if len(first_name) > threshold_span_size:
pp_text_span.append(first_name)
if len(last_name) > threshold_span_size:
pp_text_span.append(last_name)
if offset.type == "ORGANIZATION":
pm_text_span.append(text_span)
short_org_name = remove_org_type(text_span).strip()
if (len(short_org_name) > 0) and (short_org_name != text_span):
pm_text_span.append(short_org_name)
pp_matcher = AcoraMatcher(content=pp_text_span, ignore_case=True)
pm_matcher = AcoraMatcher(content=pm_text_span, ignore_case=True)
results = list()
for text, offset in zip(texts, offsets):
results.append(pp_matcher.get_matches(text=text, tag="PERS") +
pm_matcher.get_matches(text=text, tag="ORGANIZATION") +
offset)
return results
flags=regex.VERSION1
| regex.IGNORECASE)
def remove_org_type(original_text: str) -> str:
"""
Remove corporation type name
:param original_text: Name of company included its type
:return: the cleaned string
"""
return remove_org_type_pattern.sub(repl="", string=original_text).strip()
key_words_matcher = AcoraMatcher(content=[
"Monsieur", "Madame", "Mme", "monsieur", "madame", "la société", "Me",
"Maitre", "Maître"
],
ignore_case=False)
def remove_key_words(text: str, offsets: List[Offset],
rate: int) -> Tuple[str, List[Offset]]:
"""
Modify text to remove some key words, making the learning harder and the model more robust.
:param text: original paragraph as a string
:param offsets: list of extracted offsets
:param rate: chance as an integer between 1 and 100 that a key word is removed
:return: a tuple (new_text, offsets)
"""
words_to_delete_offsets: List[Offset] = key_words_matcher.get_matches(
text=text, tag="TO_DELETE")
class FrequentEntities(object):
matcher = None
frequent_entities_dict = None
def __init__(self, path_trainset: str, threshold_occurrences: int,
type_name_to_not_load: List[str], load_data: bool):
"""
Build an Acora matcher based on the dict of frequent entities
:param path_trainset: path to a file storing the entity
:param threshold_occurrences: minimum number of occurences of the entity
:param type_name_to_not_load: type of entities that should not be loaded to avoid fake match
:param load_data: boolean to decide if data should be loaded or not
:return: an Acora matcher matcher
"""
if load_data:
self.frequent_entities_dict = self.__read_frequent_entities(
path_trainset=path_trainset,
threshold_occurrences=threshold_occurrences,
type_name_to_not_load=type_name_to_not_load)
self.matcher = AcoraMatcher(content=list(
self.frequent_entities_dict.keys()),
ignore_case=True)
else:
self.matcher = AcoraMatcher(content=["!@#$%^&*()"],
ignore_case=True)
@classmethod
def test_builder(cls, content: Dict[str, str]):
"""
Build an instance of this object for tests.
In particular, don't try to read saved data
:param content: a dictionary of entities to load
:return: an instance of FrequentEntities class
"""
instance = FrequentEntities(path_trainset="",
threshold_occurrences=0,
load_data=False,
type_name_to_not_load=[])
instance.frequent_entities_dict = content
instance.matcher = AcoraMatcher(content=list(content.keys()),
ignore_case=True)
return instance
@staticmethod
def __read_frequent_entities(
path_trainset: str, threshold_occurrences: int,
type_name_to_not_load: List[str]) -> Dict[str, str]:
"""
Analyze recognized entities and return those over a defined threshold in a dict entity -> type_name
"""
try:
with open(path_trainset, 'rb') as f:
data = pickle.load(file=f)
def get_default_dict_value() -> Dict[str, set]:
default_dict_value = dict([(token, set())
for token in entity_types])
# default_dict_value['general_count'] = 0
return default_dict_value
exhaustive_dict = dict()
for case_id, text, entities in data:
for start_offset, end_offset, type_name in entities:
entity_span: str = text[start_offset:end_offset].lower()
current_count: Dict[str, set] = exhaustive_dict.get(
entity_span, get_default_dict_value())
current_count[type_name].add(case_id)
exhaustive_dict[entity_span] = current_count
final_list = list()
for entity_span, dict_counts in exhaustive_dict.items():
max_count = 0
max_type_name = None
for type_name, case_ids in dict_counts.items():
current_count = len(case_ids)
if current_count > max_count:
max_type_name = type_name
max_count = current_count
if (max_count > threshold_occurrences) and \
(len(entity_span) > 3) \
and (max_type_name not in type_name_to_not_load):
final_list.append((entity_span, max_type_name))
return dict(final_list)
except:
warnings.warn("Empty dict of frequent entities", Warning)
return dict()
def get_matches(self, text: str) -> List[Offset]:
"""
Find matches of frequent entities in the provided text
:param text: original text
:return: a list of offsets
"""
match_results = self.matcher.findall(text)
entities = list()
for match_text, start_offset in match_results:
end_offset = start_offset + len(match_text)
entity_span = text[start_offset:end_offset]
# end_offset is one character after the end of the selection,
# so it can be equal to the last charcter offset of the text + 1
last_char_ok = (end_offset
== len(text)) or (not text[end_offset].isalnum())
first_char_ok = (start_offset == 0 or not text[start_offset - 1].isalnum()) and \
(text[start_offset].isupper() or text[start_offset].isdecimal())
if first_char_ok and last_char_ok:
type_name = self.frequent_entities_dict[entity_span.lower()]
entities.append(Offset(start_offset, end_offset, type_name))
return entities
class FirstName:
first_name_dict = None
matcher = None
def __init__(self, ignore_case: bool):
"""
Build a matcher of first name based on a French names dictionary
:type ignore_case: True to ignore case during matching
:return: Acora matcher
"""
config = get_config_default()
file1 = config["first_name_dict_1"]
file2 = config["first_name_dict_2"]
firs_name = set()
with open(file1) as f1:
for line in f1.readlines():
fields = line.split(";")
# all names start with a Upcase letter and finishes with a space
text = fields[3].strip()
if len(text) >= 4:
firs_name.add(text)
with open(file2, encoding="ISO-8859-1") as f2:
for line in f2.readlines():
fields = line.split(";")
text = fields[0].strip()
if len(text) >= 4:
firs_name.add(get_title_case(text))
to_remove = [
"Elle", "France", "Mercedes", "Paris", "Alger", "Oran", "Sans"
]
for item_to_remove in to_remove:
firs_name.remove(item_to_remove)
self.first_name_dict = firs_name
self.matcher = AcoraMatcher(content=list(self.first_name_dict),
ignore_case=ignore_case)
def get_matches(self, text: str) -> List[Offset]:
"""
Find match of first name in a text
:param text: original text
:return: list of offsets
"""
offsets = self.matcher.get_matches(text=text, tag="PERS")
# names include a space so we fix the point by removing 1 to the offset
results = [
Offset(offset.start, offset.end - 1, offset.type)
for offset in offsets
]
return results
def contain_first_names(self, text: str) -> bool:
"""
Check if a text contains a known first name
:param text: original text
:return: True if it contains a first name
"""
matches = self.get_matches(text=text)
for offset in matches:
if (offset.end
== len(text) - 1) or (not text[offset.end + 1].isalpha()):
return True
return False