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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Fine-grained Named Entity Recognition in Legal Documents"""
from __future__ import absolute_import, division, print_function
import datasets
_CITATION = """\
@inproceedings{leitner2019fine,
author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
title = {{Fine-grained Named Entity Recognition in Legal Documents}},
booktitle = {Semantic Systems. The Power of AI and Knowledge
Graphs. Proceedings of the 15th International Conference
(SEMANTiCS 2019)},
year = 2019,
editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria
Maleshkova and Tassilo Pellegrini and Harald Sack and York
Sure-Vetter},
keywords = {aip},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
number = {11702},
address = {Karlsruhe, Germany},
month = 9,
note = {10/11 September 2019},
pages = {272--287},
pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}
}
"""
_DESCRIPTION = """\
We describe a dataset developed for Named Entity Recognition in German federal court decisions.
It consists of approx. 67,000 sentences with over 2 million tokens.
The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes:
person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law,
ordinance, European legal norm, regulation, contract, court decision, and legal literature.
The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions.
The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format,
was developed for training an NER service for German legal documents in the EU project Lynx.
"""
_URL = "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler.conll"
class Ler(datasets.GeneratorBasedBuilder):
"""
We describe a dataset developed for Named Entity Recognition in German federal court decisions.
It consists of approx. 67,000 sentences with over 2 million tokens.
The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes:
person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law,
ordinance, European legal norm, regulation, contract, court decision, and legal literature.
The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions.
The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format,
was developed for training an NER service for German legal documents in the EU project Lynx.
"""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"id": datasets.Value("int32"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-RR",
"I-RR",
"B-AN",
"I-AN",
"B-LD",
"I-LD",
"B-ST",
"I-ST",
"B-STR",
"I-STR",
"B-LDS",
"I-LDS",
"B-ORG",
"I-ORG",
"B-UN",
"I-UN",
"B-INN",
"I-INN",
"B-GRT",
"I-GRT",
"B-MRK",
"I-MRK",
"B-GS",
"I-GS",
"B-VO",
"I-VO",
"B-EUN",
"I-EUN",
"B-VS",
"I-VS",
"B-VT",
"I-VT",
"B-RS",
"I-RS",
"B-LIT",
"I-LIT",
]
)
),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=datasets.info.SupervisedKeysData(input="tokens", output="ner_tags"),
# Homepage of the dataset for documentation
homepage="https://github.com/elenanereiss/Legal-Entity-Recognition",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_file = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_file},
),
]
def _generate_examples(self, filepath):
""" Yields examples. """
with open(filepath, "r", encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line == "" or line == "\n":
if tokens:
yield guid, {"id": guid, "tokens": tokens, "ner_tags": ner_tags}
guid += 1
tokens = []
ner_tags = []
else:
# conll2002 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[1].rstrip())
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