Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
ArXiv:
Tags:
relation extraction
License:
| # coding=utf-8 | |
| # Copyright 2022 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. | |
| """The Google-IISc Distant Supervision (GIDS) dataset for distantly-supervised relation extraction""" | |
| import csv | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{bassignana-plank-2022-crossre, | |
| title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction", | |
| author = "Bassignana, Elisa and Plank, Barbara", | |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", | |
| year = "2022", | |
| publisher = "Association for Computational Linguistics" | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction. | |
| GIDS is seeded from the human-judged Google relation extraction corpus. | |
| """ | |
| _HOMEPAGE = "" | |
| _LICENSE = "" | |
| # The HuggingFace dataset library don't host the datasets but only point to the original files | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URLs = { | |
| "train": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/train.tsv", | |
| "validation": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/dev.tsv", | |
| "test": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/test.tsv", | |
| } | |
| _VERSION = datasets.Version("1.0.0") | |
| _CLASS_LABELS = [ | |
| "NA", | |
| "/people/person/education./education/education/institution", | |
| "/people/person/education./education/education/degree", | |
| "/people/person/place_of_birth", | |
| "/people/deceased_person/place_of_death" | |
| ] | |
| def replace_underscore_in_span(text, start, end): | |
| cleaned_text = text[:start] + text[start:end].replace("_", " ") + text[end:] | |
| return cleaned_text | |
| class GIDS(datasets.GeneratorBasedBuilder): | |
| """Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction.""" | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="gids", version=_VERSION, description="GIDS dataset." | |
| ), | |
| datasets.BuilderConfig( | |
| name="gids_formatted", version=_VERSION, description="Formatted GIDS dataset." | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "gids" # type: ignore | |
| def _info(self): | |
| if self.config.name == "gids_formatted": | |
| features = datasets.Features( | |
| { | |
| "token": datasets.Sequence(datasets.Value("string")), | |
| "subj_start": datasets.Value("int32"), | |
| "subj_end": datasets.Value("int32"), | |
| "obj_start": datasets.Value("int32"), | |
| "obj_end": datasets.Value("int32"), | |
| "relation": datasets.ClassLabel(names=_CLASS_LABELS), | |
| } | |
| ) | |
| else: | |
| features = datasets.Features( | |
| { | |
| "sentence": datasets.Value("string"), | |
| "subj_id": datasets.Value("string"), | |
| "obj_id": datasets.Value("string"), | |
| "subj_text": datasets.Value("string"), | |
| "obj_text": datasets.Value("string"), | |
| "relation": datasets.ClassLabel(names=_CLASS_LABELS) | |
| } | |
| ) | |
| 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=features, # Here we define them above because they are different between the two configurations | |
| # 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=None, | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| downloaded_files = dl_manager.download_and_extract(_URLs) | |
| return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]}) | |
| for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]] | |
| def _generate_examples(self, filepath): | |
| """Yields examples.""" | |
| # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. | |
| # It is in charge of opening the given file and yielding (key, example) tuples from the dataset | |
| # The key is not important, it's more here for legacy reason (legacy from tfds) | |
| if self.config.name == "gids_formatted": | |
| from spacy.lang.en import English | |
| word_splitter = English() | |
| else: | |
| word_splitter = None | |
| with open(filepath, encoding="utf-8") as f: | |
| data = csv.reader(f, delimiter="\t") | |
| for id_, example in enumerate(data): | |
| text = example[5].strip()[:-9].strip() # remove '###END###' from text, | |
| subj_text = example[2] | |
| obj_text = example[3] | |
| rel_type = example[4] | |
| if self.config.name == "gids_formatted": | |
| subj_char_start = text.find(subj_text) | |
| assert subj_char_start != -1, f"Did not find <{subj_text}> in the text" | |
| subj_char_end = subj_char_start + len(subj_text) | |
| obj_char_start = text.find(obj_text) | |
| assert obj_char_start != -1, f"Did not find <{obj_text}> in the text" | |
| obj_char_end = obj_char_start + len(obj_text) | |
| text = replace_underscore_in_span(text, subj_char_start, subj_char_end) | |
| text = replace_underscore_in_span(text, obj_char_start, obj_char_end) | |
| doc = word_splitter(text) | |
| word_tokens = [t.text for t in doc] | |
| subj_span = doc.char_span(subj_char_start, subj_char_end, alignment_mode="expand") | |
| obj_span = doc.char_span(obj_char_start, obj_char_end, alignment_mode="expand") | |
| yield id_, { | |
| "token": word_tokens, | |
| "subj_start": subj_span.start, | |
| "subj_end": subj_span.end, | |
| "obj_start": obj_span.start, | |
| "obj_end": obj_span.end, | |
| "relation": rel_type, | |
| } | |
| else: | |
| yield id_, { | |
| "sentence": text, | |
| "subj_id": example[0], | |
| "obj_id": example[1], | |
| "subj_text": subj_text, | |
| "obj_text": obj_text, | |
| "relation": rel_type, | |
| } | |