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| """Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition""" |
|
|
| import datasets |
|
|
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| _CITATION = """\ |
| @inproceedings{tjong-kim-sang-2002-introduction, |
| title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition", |
| author = "Tjong Kim Sang, Erik F.", |
| booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)", |
| year = "2002", |
| url = "https://www.aclweb.org/anthology/W02-2024", |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. |
| |
| Example: |
| [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . |
| |
| The shared task of CoNLL-2002 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 participants of the shared task will be offered training and test data for at least two languages. |
| They will use the data for developing a named-entity recognition system that includes a machine learning component. |
| Information sources other than the training data may be used in this shared task. |
| We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training). |
| |
| The train/validation/test sets are available in Spanish and Dutch. |
| |
| For more details see https://www.clips.uantwerpen.be/conll2002/ner/ and https://www.aclweb.org/anthology/W02-2024/ |
| """ |
|
|
| _URL = "https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/" |
| _ES_TRAINING_FILE = "esp.train" |
| _ES_DEV_FILE = "esp.testa" |
| _ES_TEST_FILE = "esp.testb" |
| _NL_TRAINING_FILE = "ned.train" |
| _NL_DEV_FILE = "ned.testa" |
| _NL_TEST_FILE = "ned.testb" |
|
|
|
|
| class Conll2002Config(datasets.BuilderConfig): |
| """BuilderConfig for Conll2002""" |
|
|
| def __init__(self, **kwargs): |
| """BuilderConfig forConll2002. |
| |
| Args: |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(Conll2002Config, self).__init__(**kwargs) |
|
|
|
|
| class Conll2002(datasets.GeneratorBasedBuilder): |
| """Conll2002 dataset.""" |
|
|
| BUILDER_CONFIGS = [ |
| Conll2002Config(name="es", version=datasets.Version("1.0.0"), description="Conll2002 Spanish dataset"), |
| Conll2002Config(name="nl", version=datasets.Version("1.0.0"), description="Conll2002 Dutch dataset"), |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "document_id": datasets.Value("int32"), |
| "sentence_id": datasets.Value("int32"), |
| "tokens": datasets.Sequence(datasets.Value("string")), |
| "pos_tags": datasets.Sequence( |
| datasets.features.ClassLabel( |
| names=[ |
| "AO", |
| "AQ", |
| "CC", |
| "CS", |
| "DA", |
| "DE", |
| "DD", |
| "DI", |
| "DN", |
| "DP", |
| "DT", |
| "Faa", |
| "Fat", |
| "Fc", |
| "Fd", |
| "Fe", |
| "Fg", |
| "Fh", |
| "Fia", |
| "Fit", |
| "Fp", |
| "Fpa", |
| "Fpt", |
| "Fs", |
| "Ft", |
| "Fx", |
| "Fz", |
| "I", |
| "NC", |
| "NP", |
| "P0", |
| "PD", |
| "PI", |
| "PN", |
| "PP", |
| "PR", |
| "PT", |
| "PX", |
| "RG", |
| "RN", |
| "SP", |
| "VAI", |
| "VAM", |
| "VAN", |
| "VAP", |
| "VAS", |
| "VMG", |
| "VMI", |
| "VMM", |
| "VMN", |
| "VMP", |
| "VMS", |
| "VSG", |
| "VSI", |
| "VSM", |
| "VSN", |
| "VSP", |
| "VSS", |
| "Y", |
| "Z", |
| ] |
| ) |
| if self.config.name == "es" |
| else datasets.features.ClassLabel( |
| names=["Adj", "Adv", "Art", "Conj", "Int", "Misc", "N", "Num", "Prep", "Pron", "Punc", "V"] |
| ) |
| ), |
| "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="https://www.aclweb.org/anthology/W02-2024/", |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| urls_to_download = { |
| "train": f"{_URL}{_ES_TRAINING_FILE if self.config.name == 'es' else _NL_TRAINING_FILE}", |
| "dev": f"{_URL}{_ES_DEV_FILE if self.config.name == 'es' else _NL_DEV_FILE}", |
| "test": f"{_URL}{_ES_TEST_FILE if self.config.name == 'es' else _NL_TEST_FILE}", |
| } |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) |
|
|
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
| ] |
|
|
| def _generate_examples(self, filepath): |
| logger.info("⏳ Generating examples from = %s", filepath) |
| with open(filepath, encoding="utf-8") as f: |
| guid = 0 |
| document_id = 0 |
| sentence_id = 0 |
| tokens = [] |
| pos_tags = [] |
| ner_tags = [] |
| for line in f: |
| if line.startswith("-DOCSTART-") or line == "" or line == "\n": |
| if line.startswith("-DOCSTART-"): |
| document_id += 1 |
| sentence_id = 0 |
| if tokens: |
| yield guid, { |
| "id": str(guid), |
| "document_id": document_id, |
| "sentence_id": sentence_id, |
| "tokens": tokens, |
| "pos_tags": pos_tags, |
| "ner_tags": ner_tags, |
| } |
| sentence_id += 1 |
| guid += 1 |
| tokens = [] |
| pos_tags = [] |
| ner_tags = [] |
| else: |
| |
| splits = line.split(" ") |
| tokens.append(splits[0]) |
| pos_tags.append(splits[1]) |
| ner_tags.append(splits[2].rstrip()) |
| if tokens: |
| |
| yield guid, { |
| "id": str(guid), |
| "document_id": document_id, |
| "sentence_id": sentence_id, |
| "tokens": tokens, |
| "pos_tags": pos_tags, |
| "ner_tags": ner_tags, |
| } |
|
|
