ipm_nel.py

# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""Named Entity Linking for Twitter (English"""

import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{derczynski2015analysis,
  title={Analysis of named entity recognition and linking for tweets},
  author={Derczynski, Leon and Maynard, Diana and Rizzo, Giuseppe and Van Erp, Marieke and Gorrell, Genevieve and Troncy, Rapha{\"e}l and Petrak, Johann and Bontcheva, Kalina},
  journal={Information Processing \& Management},
  volume={51},
  number={2},
  pages={32--49},
  year={2015},
  publisher={Elsevier}
}
"""

_DESCRIPTION = """\
This data is for the task of named entity recognition and linking/disambiguation over tweets. It comprises
the addition of an entity URI layer on top of an NER-annotated tweet dataset. The task is to detect entities
and then provide a correct link to them in DBpedia, thus disambiguating otherwise ambiguous entity surface
forms; for example, this means linking "Paris" to the correct instance of a city named that (e.g. Paris, 
France vs. Paris, Texas).

The data concentrates on ten types of named entities: company, facility, geographic location, movie, musical
artist, person, product, sports team, TV show, and other.

The file is tab separated, in CoNLL format, with line breaks between tweets.
Data preserves the tokenisation used in the Ritter datasets.
PoS labels are not present for all tweets, but where they could be found in the Ritter
data, they're given. In cases where a URI could not be agreed, or was not present in
DBpedia, there is a NIL. See the paper for a full description of the methodology.

For more details see http://www.derczynski.com/papers/ner_single.pdf or https://www.sciencedirect.com/science/article/abs/pii/S0306457314001034
"""

_URL = "http://www.derczynski.com/resources/ipm_nel.tar.gz"
_TRAINING_FILE = "ipm_nel_corpus/ipm_nel.conll"


class IpmNelConfig(datasets.BuilderConfig):
    """BuilderConfig for IPM NEL"""

    def __init__(self, **kwargs):
        """BuilderConfig for IPM NEL.

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


class IpmNel2003(datasets.GeneratorBasedBuilder):
    """IpmNel2003 dataset."""

    BUILDER_CONFIGS = [
        IpmNelConfig(name="ipm_nel", version=datasets.Version("1.0.0"), description="IPM NEL dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "uris": datasets.Value("string"),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-company",
                                "B-facility",
                                "B-geo-loc",
                                "B-movie",
                                "B-musicartist",
                                "B-other",
                                "B-person",
                                "B-product",
                                "B-sportsteam",
                                "B-tvshow",
                                "I-company",
                                "I-facility",
                                "I-geo-loc",
                                "I-movie",
                                "I-musicartist",
                                "I-other",
                                "I-person",
                                "I-product",
                                "I-sportsteam",
                                "I-tvshow",
                            ]
                        )
                    ),

                }
            ),
            supervised_keys=None,
            homepage="https://www.sciencedirect.com/science/article/pii/S0306457314001034",
            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),
        }

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

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