Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
other
Source Datasets:
original
License:
File size: 9,276 Bytes
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import os
from glob import glob
from pathlib import Path
import numpy as np
import pandas as pd
from web_anno_tsv import open_web_anno_tsv
from web_anno_tsv.web_anno_tsv import ReadException, Annotation
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
annotation_labels = {'ADDRESS': ['building', 'city', 'country', 'place', 'postcode', 'street', 'territory'],
'AMOUNT': ['unit', 'value'],
'DATE': ['year', 'standard abbreviation', 'month', 'day of the week', 'day', 'calender event'],
'PERSON': ['age', 'email', 'ethnic category', 'family name', 'financial', 'given name – female',
'given name – male',
'health insurance number', 'id document number', 'initial name', 'marital status',
'medical record number',
'nationality', 'profession', 'role', 'social security number', 'title', 'url'],
'ORGANISATION': [],
'TIME': [],
'VEHICLE': ['build year', 'colour', 'license plate number', 'model', 'type']}
# make all labels upper case
annotation_labels = {key.upper(): [label.upper() for label in labels] for key, labels in annotation_labels.items()}
print(annotation_labels)
print("coarse_grained:", list(annotation_labels.keys()))
print("fine_grained:",
[finegrained for finegrained in [finegrained_list for finegrained_list in annotation_labels.values()]])
base_path = Path("extracted")
# TODO future work can add these datasets too to make it larger
special_paths = {
"EL": ["EL/ANNOTATED_DATA/LEGAL/AREIOSPAGOS1/annotated/full_dataset"],
"EN": ["EN/ANNOTATED_DATA/ADMINISTRATIVE-LEGAL/annotated/full_dataset"],
"FR": ["FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION1/annotated/full_dataset/Civil",
"FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION1/annotated/full_dataset/Commercial",
"FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION1/annotated/full_dataset/Criminal",
"FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION2/annotated/full_dataset",
"FR/ANNOTATED_DATA/MEDICAL/CAS1/annotated/full_dataset"],
"IT": ["IT/ANNOTATED_DATA/Corte_Suprema_di_Cassazione/annotated"],
"MT": ["MT/ANNOTATED_DATA/ADMINISTRATIVE/annotated/full_dataset",
"MT/ANNOTATED_DATA/GENERAL_NEWS/News_1/annotated/full_dataset",
"MT/ANNOTATED_DATA/LEGAL/Jurisprudence_1/annotated/full_dataset"],
}
def get_path(language):
return base_path / language / "ANNOTATED_DATA/EUR_LEX/annotated/full_dataset"
def get_coarse_grained_for_fine_grained(label):
for coarse_grained, fine_grained_set in annotation_labels.items():
if label in fine_grained_set:
return coarse_grained
return None # raise ValueError(f"Did not find fine_grained label {label}")
def is_fine_grained(label):
for coarse_grained, fine_grained_set in annotation_labels.items():
if label.upper() in fine_grained_set:
return True
return False
def is_coarse_grained(label):
return label.upper() in annotation_labels.keys()
class HashableAnnotation(Annotation):
def __init__(self, annotation):
super()
self.label = annotation.label
self.start = annotation.start
self.stop = annotation.stop
self.text = annotation.text
def __eq__(self, other):
return self.label == other.label and self.start == other.start and self.stop == other.stop and self.text == other.text
def __hash__(self):
return hash(('label', self.label, 'start', self.start, 'stop', self.stop, 'text', self.text))
def get_token_annotations(token, annotations):
annotations = list(dict.fromkeys([HashableAnnotation(ann) for ann in annotations])) # remove duplicate annotations
coarse_grained = "O"
fine_grained = "o"
for annotation in annotations:
label = annotation.label
# if token.start == annotation.start and token.stop == annotation.stop: # fine_grained annotation
if token.start >= annotation.start and token.stop <= annotation.stop: # course_grained annotation
# we don't support multilabel annotations for each token for simplicity.
# So when a token already has an annotation for either coarse or fine grained, we don't assign new ones.
if coarse_grained == "O" and is_coarse_grained(label):
coarse_grained = label
elif fine_grained == "o" and is_fine_grained(label):
# some DATE are mislabeled as day but it is hard to correct this. So we ignore it
fine_grained = label
return coarse_grained.upper(), fine_grained.upper()
def generate_IOB_labelset(series, casing_function):
last_ent = ""
new_series = []
for ent in series:
if ent in ["o", "O"]:
ent_to_add = ent
else:
if ent != last_ent: # we are the first one
ent_to_add = "B-" + ent
else:
ent_to_add = "I-" + ent
new_series.append(casing_function(ent_to_add))
last_ent = ent
return new_series
def get_annotated_sentence(result_sentence, sentence):
result_sentence["tokens"] = []
result_sentence["coarse_grained"] = []
result_sentence["fine_grained"] = []
for k, token in enumerate(sentence.tokens):
coarse_grained, fine_grained = get_token_annotations(token, sentence.annotations)
token = token.text.replace(u'\xa0', u' ').strip() # replace non-breaking spaces
if token: # remove empty tokens (only consisted of whitespace before
result_sentence["tokens"].append(token)
result_sentence["coarse_grained"].append(coarse_grained)
result_sentence["fine_grained"].append(fine_grained)
result_sentence["coarse_grained"] = generate_IOB_labelset(result_sentence["coarse_grained"], str.upper)
result_sentence["fine_grained"] = generate_IOB_labelset(result_sentence["fine_grained"], str.upper)
return result_sentence
languages = sorted([Path(file).stem for file in glob(str(base_path / "*"))])
def parse_files(language):
data_path = get_path(language.upper())
result_sentences = []
not_parsable_files = 0
file_names = sorted(list(glob(str(data_path / "*.tsv"))))
for file in file_names:
try:
with open_web_anno_tsv(file) as f:
for i, sentence in enumerate(f):
result_sentence = {"language": language, "type": "EUR-LEX",
"file_name": Path(file).stem, "sentence_number": i}
result_sentence = get_annotated_sentence(result_sentence, sentence)
result_sentences.append(result_sentence)
print(f"Successfully parsed file {file}")
except ReadException as e:
print(f"Could not parse file {file}")
not_parsable_files += 1
print("Not parsable files: ", not_parsable_files)
return pd.DataFrame(result_sentences), not_parsable_files
stats = []
train_dfs, validation_dfs, test_dfs = [], [], []
for language in languages:
language = language.lower()
print(f"Parsing language {language}")
df, not_parsable_files = parse_files(language)
file_names = df.file_name.unique()
# df.coarse_grained.apply(lambda x: print(set(x)))
# split by file_name
num_fn = len(file_names)
train_fn, validation_fn, test_fn = np.split(np.array(file_names), [int(.8 * num_fn), int(.9 * num_fn)])
lang_train = df[df.file_name.isin(train_fn)]
lang_validation = df[df.file_name.isin(validation_fn)]
lang_test = df[df.file_name.isin(test_fn)]
train_dfs.append(lang_train)
validation_dfs.append(lang_validation)
test_dfs.append(lang_test)
lang_stats = {"language": language}
lang_stats["# train files"] = len(train_fn)
lang_stats["# validation files"] = len(validation_fn)
lang_stats["# test files"] = len(test_fn)
lang_stats["# train sentences"] = len(lang_train.index)
lang_stats["# validation sentences"] = len(lang_validation.index)
lang_stats["# test sentences"] = len(lang_test.index)
stats.append(lang_stats)
stat_df = pd.DataFrame(stats)
print(stat_df.to_markdown(index=False))
train = pd.concat(train_dfs)
validation = pd.concat(validation_dfs)
test = pd.concat(test_dfs)
df = pd.concat([train, validation, test])
print(f"The final coarse grained tagset (in IOB notation) is the following: "
f"`{list(df.coarse_grained.explode().unique())}`")
print(f"The final fine grained tagset (in IOB notation) is the following: "
f"`{list(df.fine_grained.explode().unique())}`")
# save splits
def save_splits_to_jsonl(config_name):
# save to jsonl files for huggingface
if config_name: os.makedirs(config_name, exist_ok=True)
train.to_json(os.path.join(config_name, "train.jsonl"), lines=True, orient="records", force_ascii=False)
validation.to_json(os.path.join(config_name, "validation.jsonl"), lines=True, orient="records", force_ascii=False)
test.to_json(os.path.join(config_name, "test.jsonl"), lines=True, orient="records", force_ascii=False)
save_splits_to_jsonl("")
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