bodo-pos-conll.py

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{bododataset2022v1,
	title = {Bodo Dataset: A comprehensive list of Bodo Datasets},
  	author = {Sanjib Narzary},
	booktitle = {Alayaran Dataset Repository},
	url = {http://get.alayaran.com},
	year = {2022},
}
"""

_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 = "http://get.alayaran.com/pos/bodo-pos-conll/bodo-pos.zip"
_TRAINING_FILE = "train-pos.txt"
_DEV_FILE = "valid-pos.txt"
_TEST_FILE = "test-pos.txt"


class BodoPoSConll2003Config(datasets.BuilderConfig):
    """BuilderConfig for Conll2003"""

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

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(BodoPoSConll2003Config, self).__init__(**kwargs)


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

    BUILDER_CONFIGS = [
        BodoPoSConll2003Config(name="bodo-pos-conll-2003", version=datasets.Version("1.0.0"), description="Bodo PoS Conll2003 dataset"),
    ]

    def _info(self):
        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=[
                                'RD_UNK', 
                                'DM_DMD', 
                                'N_NNV', 
                                'QT_QTO', 
                                'N_NST', 
                                'PR_PRC', 
                                'CC_CCS', 
                                'RP_NEG', 
                                'QT_QTF', 
                                'N_NNP', 
                                'CC_CCD', 
                                'PR_PRQ', 
                                'DM_DMR', 
                                'QT_QTC', 
                                'DM_DMI', 
                                'PR_PRF', 
                                'RB', 
                                'PSP', 
                                'V_VAUX_VF', 
                                'PR_PRP', 
                                'RD_RDF', 
                                'RP_RPD', 
                                'JJ', 
                                'RP_INJ', 
                                'V_VM', 
                                'V_VM_VF', 
                                'PR_PRL', 
                                'RD_PUNC', 
                                'RP_INTF', 
                                'DM_DMQ', 
                                'RD_ECH', 
                                'RD_SYM', 
                                'N_NN', 
                                'PR_PRI', 
                                'V_VM_VNF', 
                                'V_VAUX',
                            ]
                        )
                    ),
                    "chunk_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-ADJP",
                                "I-ADJP",
                                "B-ADVP",
                                "I-ADVP",
                                "B-CONJP",
                                "I-CONJP",
                                "B-INTJ",
                                "I-INTJ",
                                "B-LST",
                                "I-LST",
                                "B-NP",
                                "I-NP",
                                "B-PP",
                                "I-PP",
                                "B-PRT",
                                "I-PRT",
                                "B-SBAR",
                                "I-SBAR",
                                "B-UCP",
                                "I-UCP",
                                "B-VP",
                                "I-VP",
                            ]
                        )
                    ),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-PER",
                                "I-PER",
                                "B-ORG",
                                "I-ORG",
                                "B-LOC",
                                "I-LOC",
                                "B-MISC",
                                "I-MISC",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="http://get.alayaran.com",
            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('O')
                    ner_tags.append('O')
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "pos_tags": pos_tags,
                    "chunk_tags": chunk_tags,
                    "ner_tags": ner_tags,
                }