| 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" |
|
|
|
|
| @dataclass |
| 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, |
| |
| 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 |
|
|
