Datasets:
Tasks:
Token Classification
Sub-tasks:
parsing
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
| import glob | |
| import json | |
| import os | |
| from dataclasses import dataclass | |
| import datasets | |
| from datasets import BuilderConfig, SplitGenerator | |
| _CITATION = """\ | |
| @article{yang2018scidtb, | |
| title={Scidtb: Discourse dependency treebank for scientific abstracts}, | |
| author={Yang, An and Li, Sujian}, | |
| journal={arXiv preprint arXiv:1806.03653}, | |
| year={2018} | |
| } | |
| """ | |
| _DESCRIPTION = """Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question | |
| answering. SciDTB is a domain-specific discourse treebank annotated on scientific articles. | |
| Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is | |
| flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, | |
| annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating | |
| discourse dependency parsers, on which we provide several baselines as fundamental work.""" | |
| _URL = "https://codeload.github.com/PKU-TANGENT/SciDTB/zip/refs/heads/master" | |
| _HOMEPAGE = "https://github.com/PKU-TANGENT/SciDTB" | |
| class SciDTBConfig(BuilderConfig): | |
| """BuilderConfig for SciDTB.""" | |
| def __init__(self, subdirectory_mapping, encoding, **kwargs): | |
| super(SciDTBConfig, self).__init__(**kwargs) | |
| self.subdirectory_mapping = subdirectory_mapping | |
| self.encoding = encoding | |
| class SciDTBDataset(datasets.GeneratorBasedBuilder): | |
| """Scientific Discourse Treebank Dataset""" | |
| BUILDER_CONFIGS = [ | |
| SciDTBConfig( | |
| name="SciDTB", | |
| version=datasets.Version("1.0.0", ""), | |
| description=_DESCRIPTION, | |
| subdirectory_mapping={ | |
| "train": "SciDTB-master/dataset/train", | |
| "dev": "SciDTB-master/dataset/dev/gold", | |
| "test": "SciDTB-master/dataset/test/gold", | |
| }, | |
| encoding="utf-8-sig", | |
| ), | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "root": datasets.Sequence( | |
| { | |
| "id": datasets.Value("int32"), | |
| "parent": datasets.Value("int32"), | |
| "text": datasets.Value("string"), | |
| "relation": datasets.Value("string"), | |
| } | |
| ), | |
| "file_name": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_URL) | |
| return [ | |
| SplitGenerator( | |
| name=split, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "dir_path": os.path.join(data_dir, subdir), | |
| }, | |
| ) | |
| for split, subdir in self.config.subdirectory_mapping.items() | |
| ] | |
| def _generate_examples(self, dir_path): | |
| _files = glob.glob(f"{dir_path}/*.dep") | |
| for file_path in _files: | |
| with open(file_path, mode="r", encoding=self.config.encoding) as f: | |
| annotations = json.load(f) | |
| annotations["file_name"] = os.path.basename(file_path) | |
| yield annotations["file_name"], annotations | |