cross_re.py

# 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.
"""CrossRE is a cross-domain dataset for relation extraction"""


import json
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 = """\
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes 
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and 
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL, 
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and 
RELATED-TO.

For details, see the paper: https://arxiv.org/abs/2210.09345
"""

_HOMEPAGE = "https://github.com/mainlp/CrossRE"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "news": {
        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-train.json",
        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-dev.json",
        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-test.json",
    },
    "politics": {
        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-train.json",
        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-dev.json",
        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-test.json",
    },
    "science": {
        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-train.json",
        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-dev.json",
        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-test.json",
    },
    "music": {
        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-train.json",
        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-dev.json",
        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-test.json",
    },
    "literature": {
        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-train.json",
        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-dev.json",
        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-test.json",
    },
    "ai": {
        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-train.json",
        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-dev.json",
        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-test.json",
    },
}


class CrossRE(datasets.GeneratorBasedBuilder):
    """CrossRE is a cross-domain dataset for relation extraction"""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="news", version=VERSION,
                               description="This part of CrossRE covers data from the news domain"),
        datasets.BuilderConfig(name="politics", version=VERSION,
                               description="This part of CrossRE covers data from the politics domain"),
        datasets.BuilderConfig(name="science", version=VERSION,
                               description="This part of CrossRE covers data from the science domain"),
        datasets.BuilderConfig(name="music", version=VERSION,
                               description="This part of CrossRE covers data from the music domain"),
        datasets.BuilderConfig(name="literature", version=VERSION,
                               description="This part of CrossRE covers data from the literature domain"),
        datasets.BuilderConfig(name="ai", version=VERSION,
                               description="This part of CrossRE covers data from the AI domain"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "doc_key": datasets.Value("string"),
                "sentence": datasets.Sequence(datasets.Value("string")),
                "ner": [{
                    "id-start": datasets.Value("int32"),
                    "id-end": datasets.Value("int32"),
                    "entity-type": datasets.Value("string"),
                }],
                "relations": [{
                    "id_1-start": datasets.Value("int32"),
                    "id_1-end": datasets.Value("int32"),
                    "id_2-start": datasets.Value("int32"),
                    "id_2-end": datasets.Value("int32"),
                    "relation-type": datasets.Value("string"),
                    "Exp": datasets.Value("string"),  # Explanation of the relation type assigned
                    "Un": datasets.Value("bool"),  # Uncertainty of the annotator
                    "SA": datasets.Value("bool"),  # Syntax Ambiguity which poses a challenge for the annotator
                }]
            }
        )
        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, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # 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):
        # 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
        urls = _URLS[self.config.name]
        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]]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for row in f:
                doc = json.loads(row)
                doc_key = doc["doc_key"]
                ner = []
                for entity in doc["ner"]:
                    ner.append({
                        "id-start": entity[0],
                        "id-end": entity[1],
                        "entity-type": entity[2],
                    })
                relations = []
                for relation in doc["relations"]:
                    relations.append({
                        "id_1-start": relation[0],
                        "id_1-end": relation[1],
                        "id_2-start": relation[2],
                        "id_2-end": relation[3],
                        "relation-type": relation[4],
                        "Exp": relation[5],
                        "Un": relation[6],
                        "SA": relation[7],
                    })
                yield doc_key, {
                    "doc_key": doc_key,
                    "sentence": doc["sentence"],
                    "ner": ner,
                    "relations": relations
                }