frwiki_el.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""TODO: Add a description here."""


import re
import gzip
import json
import datasets
from pathlib import Path


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = ""

_DESCRIPTION = """\
French Wikipedia dataset for Entity Linking
"""

_HOMEPAGE = "https://github.com/GaaH/frwiki_el"

_LICENSE = "WTFPL"

_URLs = {
    "frwiki": "data/corpus.jsonl.gz",
    "entities": "data/entities.jsonl.gz",
}

_NER_CLASS_LABELS = [
    "B",
    "I",
    "O",
]

_ENTITY_TYPES = [
    "DATE",
    "PERSON",
    "GEOLOC",
    "ORG",
    "OTHER",
]


def item_to_el_features(item, title2qid):
    res = {
        "title": item['name'].replace("_", " "),
        "wikidata_id": item['wikidata_id'],
        "wikipedia_id": item['wikipedia_id'],
        "wikidata_url": item['wikidata_url'],
        "wikipedia_url": item['wikipedia_url'],
    }
    text_dict = {
        "words": [],
        "ner": [],
        "el": [],
    }
    entity_pattern = r"\[E=(.+?)\](.+?)\[/E\]"

    # start index of the previous text
    i = 0
    text = item['text']
    for m in re.finditer(entity_pattern, text):
        mention_title = m.group(1)
        mention = m.group(2)

        mention_qid = title2qid.get(mention_title.replace("_", " "), "unknown")

        mention_words = mention.split()

        j = m.start(0)
        prev_text = text[i:j].split()
        len_prev_text = len(prev_text)
        text_dict["words"].extend(prev_text)
        text_dict["ner"].extend(["O"] * len_prev_text)
        text_dict["el"].extend([None] * len_prev_text)

        text_dict["words"].extend(mention_words)

        len_mention_tail = len(mention_words) - 1
        text_dict["ner"].extend(["B"] + ["I"] * len_mention_tail)
        text_dict["el"].extend([mention_qid] + [mention_qid] * len_mention_tail)

        i = m.end(0)

    tail = text[i:].split()
    len_tail = len(tail)
    text_dict["words"].extend(tail)
    text_dict["ner"].extend(["O"] * len_tail)
    text_dict["el"].extend([None] * len_tail)
    res.update(text_dict)
    return res


class FrwikiElDataset(datasets.GeneratorBasedBuilder):
    """
    """

    VERSION = datasets.Version("0.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="frwiki", version=VERSION,
                               description="The frwiki dataset for Entity Linking"),
        datasets.BuilderConfig(name="entities", version=VERSION,
                               description="Entities and their descriptions"),
    ]

    # It's not mandatory to have a default configuration. Just use one if it make sense.
    DEFAULT_CONFIG_NAME = "frwiki"

    def _info(self):
        if self.config.name == "frwiki":
            features = datasets.Features({
                "name": datasets.Value("string"),
                "wikidata_id": datasets.Value("string"),
                "wikipedia_id": datasets.Value("string"),
                "wikipedia_url": datasets.Value("string"),
                "wikidata_url": datasets.Value("string"),
                "words": [datasets.Value("string")],
                "ner": [datasets.ClassLabel(names=_NER_CLASS_LABELS)],
                "el": [datasets.Value("string")],
            })
        elif self.config.name == "entities":
            features = datasets.Features({
                "name": datasets.Value("string"),
                "wikidata_id": datasets.Value("string"),
                "wikipedia_id": datasets.Value("string"),
                "wikipedia_url": datasets.Value("string"),
                "wikidata_url": datasets.Value("string"),
                "description": datasets.Value("string"),
            })

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            # Here we define them above because they are different between the two configurations
            features=features,
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        filepath = _URLs[self.config.name]
        # data_dir = dl_manager.download_and_extract(my_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "path": filepath,
                }
            )
        ]

    def _generate_examples(self, path):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        # entities_path = Path(data_dir, "entities.jsonl.gz")
        # corpus_path = Path(data_dir, "corpus.jsonl.gz")

        def _identiy(x):
            return x

        with gzip.open(path, "rt", encoding="UTF-8") as crps_file:
            for id, line in enumerate(crps_file):
                item = json.loads(line, parse_int=_identiy, parse_float=_identiy, parse_constant=_identiy)
                yield id, item