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"""Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition""" |
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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-2002-introduction, |
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title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition", |
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author = "Tjong Kim Sang, Erik F.", |
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booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)", |
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year = "2002", |
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url = "https://www.aclweb.org/anthology/W02-2024", |
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} |
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""" |
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_DESCRIPTION = """\ |
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Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. |
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Example: |
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[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . |
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The shared task of CoNLL-2002 concerns language-independent named entity recognition. |
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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. |
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The participants of the shared task will be offered training and test data for at least two languages. |
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They will use the data for developing a named-entity recognition system that includes a machine learning component. |
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Information sources other than the training data may be used in this shared task. |
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We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training). |
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The train/validation/test sets are available in Spanish and Dutch. |
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For more details see https://www.clips.uantwerpen.be/conll2002/ner/ and https://www.aclweb.org/anthology/W02-2024/ |
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""" |
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_URL = "https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/" |
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_ES_TRAINING_FILE = "esp.train" |
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_ES_DEV_FILE = "esp.testa" |
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_ES_TEST_FILE = "esp.testb" |
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_NL_TRAINING_FILE = "ned.train" |
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_NL_DEV_FILE = "ned.testa" |
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_NL_TEST_FILE = "ned.testb" |
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class Conll2002Config(datasets.BuilderConfig): |
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"""BuilderConfig for Conll2002""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig forConll2002. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Conll2002Config, self).__init__(**kwargs) |
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class Conll2002(datasets.GeneratorBasedBuilder): |
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"""Conll2002 dataset.""" |
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BUILDER_CONFIGS = [ |
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Conll2002Config(name="es", version=datasets.Version("1.0.0"), description="Conll2002 Spanish dataset"), |
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Conll2002Config(name="nl", version=datasets.Version("1.0.0"), description="Conll2002 Dutch 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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"AO", |
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"AQ", |
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"CC", |
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"CS", |
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"DA", |
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"DE", |
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"DD", |
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"DI", |
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"DN", |
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"DP", |
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"DT", |
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"Faa", |
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"Fat", |
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"Fc", |
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"Fd", |
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"Fe", |
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"Fg", |
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"Fh", |
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"Fia", |
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"Fit", |
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"Fp", |
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"Fpa", |
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"Fpt", |
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"Fs", |
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"Ft", |
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"Fx", |
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"Fz", |
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"I", |
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"NC", |
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"NP", |
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"P0", |
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"PD", |
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"PI", |
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"PN", |
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"PP", |
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"PR", |
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"PT", |
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"PX", |
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"RG", |
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"RN", |
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"SP", |
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"VAI", |
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"VAM", |
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"VAN", |
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"VAP", |
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"VAS", |
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"VMG", |
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"VMI", |
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"VMM", |
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"VMN", |
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"VMP", |
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"VMS", |
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"VSG", |
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"VSI", |
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"VSM", |
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"VSN", |
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"VSP", |
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"VSS", |
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"Y", |
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"Z", |
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] |
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) |
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if self.config.name == "es" |
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else datasets.features.ClassLabel( |
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names=["Adj", "Adv", "Art", "Conj", "Int", "Misc", "N", "Num", "Prep", "Pron", "Punc", "V"] |
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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/W02-2024/", |
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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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urls_to_download = { |
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"train": f"{_URL}{_ES_TRAINING_FILE if self.config.name == 'es' else _NL_TRAINING_FILE}", |
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"dev": f"{_URL}{_ES_DEV_FILE if self.config.name == 'es' else _NL_DEV_FILE}", |
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"test": f"{_URL}{_ES_TEST_FILE if self.config.name == 'es' else _NL_TEST_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_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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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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"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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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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ner_tags.append(splits[2].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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"ner_tags": ner_tags, |
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} |
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