Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hungarian
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
other
Annotations Creators:
expert-generated
Source Datasets:
original
File size: 5,485 Bytes
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from datasets import BuilderConfig, Version, GeneratorBasedBuilder, DatasetInfo, Features, Value, \
Sequence, ClassLabel, DownloadManager, SplitGenerator, Split
import datasets
import os
import textwrap
import csv
from ast import literal_eval
_DESCRIPTION = """
The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language
Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including
Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc."""
_CITATION = """"""
_FEATURES = Features(
{
"id": Value("int32"),
"tokens": Sequence(Value("string")),
"ner": Sequence(
ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
"document_id": Value("int32"),
"sentence_id": Value("int32")
}
)
class SzegedNERConfig(BuilderConfig):
"""BuilderConfig for SzegedNER."""
def __init__(
self,
features,
label_column,
data_dir,
citation,
url,
process_label=lambda x: x,
**kwargs,
):
super(SzegedNERConfig, self).__init__(version=Version("1.0.0", ""), **kwargs)
self.features = features
self.label_column = label_column
self.data_dir = data_dir
self.citation = citation
self.url = url
self.process_label = process_label
class SzegedNER(GeneratorBasedBuilder):
"""SzegedNER datasets."""
BUILDER_CONFIGS = [
SzegedNERConfig(
name="business",
description=textwrap.dedent(
"""\
The Named Entity Corpus for Hungarian is a subcorpus of the Szeged Treebank, which contains full syntactic
annotations done manually by linguist experts. A significant part of these texts has been annotated with
Named Entity class labels in line with the annotation standards used on the CoNLL-2003 shared task."""
),
features=_FEATURES,
label_column="ner_tags",
data_dir="data/business/",
citation=textwrap.dedent(_CITATION),
url="https://rgai.inf.u-szeged.hu/node/130"
),
SzegedNERConfig(
name="criminal",
description=textwrap.dedent(
"""\
The Hungarian National Corpus and its Heti Világgazdaság (HVG) subcorpus provided the basis for corpus text
selection: articles related to the topic of financially liable offences were selected and annotated for the
categories person, organization, location and miscellaneous. There are two annotated versions of the corpus.
When preparing the tag-for-meaning annotation, our linguists took into consideration the context in which
the Named Entity under investigation occurred, thus, it was not the primary sense of the Named Entity that
determined the tag (e.g. Manchester=LOC) but its contextual reference (e.g. Manchester won the Premier
League=ORG). As for tag-for-tag annotation, these cases were not differentiated: tags were always given on
the basis of the primary sense."""
),
features=_FEATURES,
label_column="ner_tags",
data_dir="data/criminal/",
citation=textwrap.dedent(_CITATION),
url="https://rgai.inf.u-szeged.hu/node/130"
)
]
def _info(self):
return DatasetInfo(
description=self.config.description,
features=self.config.features,
homepage=self.config.url,
citation=self.config.citation,
)
def _split_generators(self, dl_manager: DownloadManager):
url = f"{self.base_path}{self.config.data_dir}"
path = dl_manager.download({key: f"{url}{key}.csv" for key in ["train", "validation", "test"]})
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={"split_key": "train", "data_file": path['train']},
),
SplitGenerator(
name=Split.VALIDATION,
gen_kwargs={"split_key": "validation", "data_file": path['validation']},
),
SplitGenerator(
name=Split.TEST,
gen_kwargs={"split_key": "test", "data_file": path['test']},
)
]
def _generate_examples(self, data_file, split_key, **kwargs):
with open(data_file, encoding="utf8") as f:
reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_MINIMAL)
for n, row in enumerate(reader):
labels = literal_eval(row['ner'])
tokens = literal_eval(row['tokens'])
if len(labels) != len(tokens):
raise ValueError("Number of tokens and labels does not match")
yield n, {
"id": int(row['id']),
"tokens": tokens,
"ner": labels,
"document_id": int(row['document_id']),
"sentence_id": int(row['sentence_id'])
}
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