conll2003-v2.py

# coding=utf-8
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# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import os

import datasets
from datasets import load_dataset

logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
    author = "Tjong Kim Sang, Erik F.  and
      De Meulder, Fien",
    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
    year = "2003",
    url = "https://www.aclweb.org/anthology/W03-0419",
    pages = "142--147",
}
"""

_DESCRIPTION = """\
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.

The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.

For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
"""

# _URL = "https://github.com/lunesco/conll2003-v2/raw/0f150c8a0c7137def22655f46f5514aed8e09d24/conll2003_v2.zip"
_URL = "https://github.com/lunesco/conll2003-v2/raw/25ff141d9deb913f3c682afff97b789acda0b18b/conll2003_v3.zip"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "valid.txt"
_TEST_FILE = "test.txt"


class Conll2003Config(datasets.BuilderConfig):
    """BuilderConfig for Conll2003"""

    def __init__(self, **kwargs):
        """BuilderConfig forConll2003.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(Conll2003Config, self).__init__(**kwargs)


class Conll2003(datasets.GeneratorBasedBuilder):
    """Conll2003 dataset."""

    BUILDER_CONFIGS = [
        Conll2003Config(name="conll2003-v2", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
    ]

    def _info(self):  # 49, 23, 42 dlugosc (vs 47, 23, 9)
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "pos_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=['VAFIN', 'PPOSAT', 'NN', 'APPR', 'ADV', 'VVINF', '$.', 'NE',
       'CARD', 'TRUNC', 'XY', 'ADJA', 'ART', 'VVFIN', 'PPER', 'APPRART',
       '$[', 'VVPP', 'KON', '$,', 'PTKVZ', 'ADJD', 'PIAT', 'PRELS',
       'PTKNEG', 'VAINF', 'VMFIN', 'PTKZU', 'PROAV', 'PIDAT', 'PDS',
       'PWAV', 'PWS', 'KOUS', 'PIS', 'PRF', 'FM', 'ITJ', 'PTKANT', 'PDAT',
       'VVIZU', 'PWAT', 'APZR', 'KOKOM', 'VVIMP', 'PTKA', 'KOUI', 'APPO',
       'VAPP', 'VMINF']  # 50
                        )
                    ),
                    "chunk_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=['I-VA', 'I-PP', 'I-NN', 'I-AP', 'I-AD', 'I-VV', 'I-$.', 'I-NE',
       '-X-', 'I-CA', 'I-TR', 'I-XY', 'I-AR', 'I-$[', 'I-KO', 'I-$,',
       'I-PT', 'I-PI', 'I-PR', 'I-VM', 'I-PD', 'I-PW', 'I-FM', 'I-IT']  # 24
                        )
                    ),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=['O', 'B-organization-company', 'B-location-route',
       'B-trigger', 'B-location-stop', 'B-date', 'B-location-city',
       'B-event-cause', 'I-event-cause', 'B-time', 'I-time', 'B-number',
       'B-organization', 'I-organization', 'B-location-street',
       'I-trigger', 'B-location', 'I-location', 'I-location-city',
       'I-organization-company', 'B-duration', 'I-duration',
       'I-location-street', 'I-location-stop', 'I-location-route',
       'B-person', 'I-date', 'B-set', 'B-money', 'I-person', 'I-money',
       'B-distance', 'I-distance', 'I-number', 'B-disaster-type',
       'B-org-position', 'I-org-position', 'I-set', 'B-percent',
       'I-percent', 'I-disaster-type']  # 41
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/W03-0419/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract(_URL)
        data_files = {
            "train": os.path.join(downloaded_file, _TRAINING_FILE),
            "dev": os.path.join(downloaded_file, _DEV_FILE),
            "test": os.path.join(downloaded_file, _TEST_FILE),
        }

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

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            pos_tags = []
            chunk_tags = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "pos_tags": pos_tags,
                            "chunk_tags": chunk_tags,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        pos_tags = []
                        chunk_tags = []
                        ner_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    pos_tags.append(splits[1])
                    chunk_tags.append(splits[2])
                    ner_tags.append(splits[3].rstrip())
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "pos_tags": pos_tags,
                    "chunk_tags": chunk_tags,
                    "ner_tags": ner_tags,
                }