mobie.py

# coding=utf-8
# 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.
"""\
MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks."""

import re
from json import JSONDecodeError, JSONDecoder

import datasets


_CITATION = """\
        @inproceedings{hennig-etal-2021-mobie,
    title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
    author = "Hennig, Leonhard  and
      Truong, Phuc Tran  and
      Gabryszak, Aleksandra",
    booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
    month = "6--9 " # sep,
    year = "2021",
    address = {D{\"u}sseldorf, Germany},
    publisher = "KONVENS 2021 Organizers",
    url = "https://aclanthology.org/2021.konvens-1.22",
    pages = "223--227",
}
"""

_DESCRIPTION = """\
MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks."""

_HOMEPAGE = "https://github.com/dfki-nlp/mobie"

_LICENSE = "CC-BY 4.0"

_URLs = {
    "train": "https://github.com/DFKI-NLP/MobIE/raw/master/v1_20210811/train.jsonl.gz",
    "dev": "https://github.com/DFKI-NLP/MobIE/raw/master/v1_20210811/dev.jsonl.gz",
    "test": "https://github.com/DFKI-NLP/MobIE/raw/master/v1_20210811/test.jsonl.gz",
}


class Mobie(datasets.GeneratorBasedBuilder):
    """MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities"""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="mobie-v1_20210811", version=VERSION, description="MobIE V1"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-date",
                            "I-date",
                            "B-disaster-type",
                            "I-disaster-type",
                            "B-distance",
                            "I-distance",
                            "B-duration",
                            "I-duration",
                            "B-event-cause",
                            "I-event-cause",
                            "B-location",
                            "I-location",
                            "B-location-city",
                            "I-location-city",
                            "B-location-route",
                            "I-location-route",
                            "B-location-stop",
                            "I-location-stop",
                            "B-location-street",
                            "I-location-street",
                            "B-money",
                            "I-money",
                            "B-number",
                            "I-number",
                            "B-organization",
                            "I-organization",
                            "B-organization-company",
                            "I-organization-company",
                            "B-org-position",
                            "I-org-position",
                            "B-percent",
                            "I-percent",
                            "B-person",
                            "I-person",
                            "B-set",
                            "I-set",
                            "B-time",
                            "I-time",
                            "B-trigger",
                            "I-trigger",
                        ]
                    )
                ),
            }
        )

        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."""

        # 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
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir["train"], "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir["dev"], "split": "dev"},
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""

        NOT_WHITESPACE = re.compile(r"[^\s]")

        def decode_stacked(document, pos=0, decoder=JSONDecoder()):
            while True:
                match = NOT_WHITESPACE.search(document, pos)
                if not match:
                    return
                pos = match.start()
                try:
                    obj, pos = decoder.raw_decode(document, pos)
                except JSONDecodeError:
                    raise
                yield obj

        with open(filepath, encoding="utf-8") as f:
            raw = f.read()

        for doc in decode_stacked(raw):
            text = doc["text"]["string"]


            entity_starts = []
            for m in doc["conceptMentions"]["array"]:
                entity_starts.append(m["span"]["start"])
            for s in doc["sentences"]["array"]:
                toks = []
                lbls = []
                sid = s["id"]
                for x in s["tokens"]["array"]:
                    toks.append(text[x["span"]["start"] : x["span"]["end"]])
                    # convert to BIO
                    if x["ner"]["string"] != 'O':
                        lbls.append("B-" + x["ner"]["string"] if x["span"]["start"] in entity_starts else "I-" + x["ner"]["string"])
                    else:
                        lbls.append(x["ner"]["string"])

                yield sid, {
                    "id": sid,
                    "tokens": toks,
                    "ner_tags": lbls,
                }