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"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
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import os
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
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title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
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author = "Tjong Kim Sang, Erik F. and
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De Meulder, Fien",
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booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
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year = "2003",
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url = "https://www.aclweb.org/anthology/W03-0419",
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pages = "142--147",
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}
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"""
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_DESCRIPTION = """\
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The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
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four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
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not belong to the previous three groups.
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The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
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a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
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a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
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and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
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if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
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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
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tagging scheme, whereas the original dataset uses IOB1.
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For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
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"""
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_URL = "https://cdn-lfs.huggingface.co/datasets/leonadase/mycoll3/88f1f7111b6ccb1897ba06879b5a782f237755dfa48640610adedbe6ce098937"
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_TRAINING_FILE = "train.txt"
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_DEV_FILE = "valid.txt"
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_TEST_FILE = "test.txt"
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class Conll2003Config(datasets.BuilderConfig):
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"""BuilderConfig for Conll2003"""
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def __init__(self, **kwargs):
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"""BuilderConfig forConll2003.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(Conll2003Config, self).__init__(**kwargs)
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class Conll2003(datasets.GeneratorBasedBuilder):
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"""Conll2003 dataset."""
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BUILDER_CONFIGS = [
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Conll2003Config(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"pos_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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'"',
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"''",
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"#",
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"$",
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"(",
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")",
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",",
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".",
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":",
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"``",
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"CC",
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"CD",
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"DT",
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"EX",
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"FW",
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"IN",
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"JJ",
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"JJR",
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"JJS",
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"LS",
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"MD",
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"NN",
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"NNP",
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"NNPS",
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"NNS",
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"NN|SYM",
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"PDT",
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"POS",
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"PRP",
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"PRP$",
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"RB",
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"RBR",
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"RBS",
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"RP",
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"SYM",
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"TO",
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"UH",
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"VB",
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"VBD",
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"VBG",
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"VBN",
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"VBP",
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"VBZ",
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"WDT",
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"WP",
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"WP$",
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"WRB",
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]
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)
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),
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"chunk_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"O",
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"B-ADJP",
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"I-ADJP",
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"B-ADVP",
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"I-ADVP",
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"B-CONJP",
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"I-CONJP",
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"B-INTJ",
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"I-INTJ",
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"B-LST",
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"I-LST",
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"B-NP",
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"I-NP",
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"B-PP",
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"I-PP",
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"B-PRT",
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"I-PRT",
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"B-SBAR",
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"I-SBAR",
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"B-UCP",
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"I-UCP",
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"B-VP",
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"I-VP",
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]
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)
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),
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"ner_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"O",
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"B-PER",
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"I-PER",
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"B-ORG",
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"I-ORG",
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"B-LOC",
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"I-LOC",
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"B-MISC",
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"I-MISC",
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]
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)
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),
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}
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),
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supervised_keys=None,
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homepage="https://www.aclweb.org/anthology/W03-0419/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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downloaded_file = dl_manager.download_and_extract(_URL)
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data_files = {
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"train": os.path.join(downloaded_file, _TRAINING_FILE),
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"dev": os.path.join(downloaded_file, _DEV_FILE),
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"test": os.path.join(downloaded_file, _TEST_FILE),
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}
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
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]
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def _generate_examples(self, filepath):
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logger.info("⏳ Generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as f:
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guid = 0
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tokens = []
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pos_tags = []
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chunk_tags = []
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ner_tags = []
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for line in f:
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if line.startswith("-DOCSTART-") or line == "" or line == "\n":
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if tokens:
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yield guid, {
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"id": str(guid),
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"tokens": tokens,
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"pos_tags": pos_tags,
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"chunk_tags": chunk_tags,
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"ner_tags": ner_tags,
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}
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guid += 1
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tokens = []
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pos_tags = []
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chunk_tags = []
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ner_tags = []
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else:
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splits = line.split(" ")
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tokens.append(splits[0])
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pos_tags.append(splits[1])
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chunk_tags.append(splits[2])
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ner_tags.append(splits[3].rstrip())
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yield guid, {
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"id": str(guid),
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"tokens": tokens,
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"pos_tags": pos_tags,
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"chunk_tags": chunk_tags,
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"ner_tags": ner_tags,
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} |
