germeval_14.py

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""The GermEval 2014 NER Shared Task dataset."""


import csv

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{benikova-etal-2014-nosta,
    title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
    author = {Benikova, Darina  and
      Biemann, Chris  and
      Reznicek, Marc},
    booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
    month = {may},
    year = {2014},
    address = {Reykjavik, Iceland},
    publisher = {European Language Resources Association (ELRA)},
    url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
    pages = {2524--2531},
}
"""

_DESCRIPTION = """\
The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties:\
    - The data was sampled from German Wikipedia and News Corpora as a collection of citations.\
    - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.\
    - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines,\
      using four main NER categories with sub-structure, and annotating embeddings among NEs\
      such as [ORG FC Kickers [LOC Darmstadt]].
"""

_URLS = {
    "train": "https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P",
    "dev": "https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm",
    "test": "https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH",
}


class GermEval14Config(datasets.BuilderConfig):
    """BuilderConfig for GermEval 2014."""

    def __init__(self, **kwargs):
        """BuilderConfig for GermEval 2014.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(GermEval14Config, self).__init__(**kwargs)


class GermEval14(datasets.GeneratorBasedBuilder):
    """GermEval 2014 NER Shared Task dataset."""

    BUILDER_CONFIGS = [
        GermEval14Config(
            name="germeval_14", version=datasets.Version("2.0.0"), description="GermEval 2014 NER Shared Task dataset"
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "source": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-LOC",
                                "I-LOC",
                                "B-LOCderiv",
                                "I-LOCderiv",
                                "B-LOCpart",
                                "I-LOCpart",
                                "B-ORG",
                                "I-ORG",
                                "B-ORGderiv",
                                "I-ORGderiv",
                                "B-ORGpart",
                                "I-ORGpart",
                                "B-OTH",
                                "I-OTH",
                                "B-OTHderiv",
                                "I-OTHderiv",
                                "B-OTHpart",
                                "I-OTHpart",
                                "B-PER",
                                "I-PER",
                                "B-PERderiv",
                                "I-PERderiv",
                                "B-PERpart",
                                "I-PERpart",
                            ]
                        )
                    ),
                    "nested_ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-LOC",
                                "I-LOC",
                                "B-LOCderiv",
                                "I-LOCderiv",
                                "B-LOCpart",
                                "I-LOCpart",
                                "B-ORG",
                                "I-ORG",
                                "B-ORGderiv",
                                "I-ORGderiv",
                                "B-ORGpart",
                                "I-ORGpart",
                                "B-OTH",
                                "I-OTH",
                                "B-OTHderiv",
                                "I-OTHderiv",
                                "B-OTHpart",
                                "I-OTHpart",
                                "B-PER",
                                "I-PER",
                                "B-PERderiv",
                                "I-PERderiv",
                                "B-PERpart",
                                "I-PERpart",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://sites.google.com/site/germeval2014ner/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_files = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            current_source = ""
            current_tokens = []
            current_ner_tags = []
            current_nested_ner_tags = []
            sentence_counter = 0
            for row in data:
                if row:
                    if row[0] == "#":
                        current_source = " ".join(row[1:])
                        continue
                    id_, token, label, nested_label = row[:4]
                    current_tokens.append(token)
                    current_ner_tags.append(label)
                    current_nested_ner_tags.append(nested_label)
                else:
                    # New sentence
                    if not current_tokens:
                        # Consecutive empty lines will cause empty sentences
                        continue
                    assert len(current_tokens) == len(current_ner_tags), "💔 between len of tokens & labels"
                    assert len(current_ner_tags) == len(
                        current_nested_ner_tags
                    ), "💔 between len of labels & nested labels"
                    assert current_source, "💥 Source for new sentence was not set"
                    sentence = (
                        sentence_counter,
                        {
                            "id": str(sentence_counter),
                            "tokens": current_tokens,
                            "ner_tags": current_ner_tags,
                            "nested_ner_tags": current_nested_ner_tags,
                            "source": current_source,
                        },
                    )
                    sentence_counter += 1
                    current_tokens = []
                    current_ner_tags = []
                    current_nested_ner_tags = []
                    current_source = ""
                    yield sentence
            # Don't forget last sentence in dataset 🧐
            yield sentence_counter, {
                "id": str(sentence_counter),
                "tokens": current_tokens,
                "ner_tags": current_ner_tags,
                "nested_ner_tags": current_nested_ner_tags,
                "source": current_source,
            }