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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{pradhan2012conll, |
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title={CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes}, |
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author={Pradhan, Sameer and Moschitti, Alessandro and Xue, Nianwen and Uryupina, Olga and Zhang, Yuchen}, |
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booktitle={Joint Conference on EMNLP and CoNLL-Shared Task}, |
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pages={1--40}, |
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year={2012} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and Arabic, using the final version, v5.0, |
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of the OntoNotes corpus. It was a follow-on to the English-only task organized in 2011. Until the creation of the OntoNotes |
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corpus, resources in this sub-field of language processing were limited to noun phrase coreference, often on a restricted |
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set of entities, such as the ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not |
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restricted to noun phrases or to a specified set of entity types, and covers multiple languages. OntoNotes also provides |
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additional layers of integrated annotation, capturing additional shallow semantic structure. This paper describes the |
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OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the |
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format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating |
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systems. The task of coreference has had a complex evaluation history. Potentially many evaluation conditions, have, in the past, |
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made it difficult to judge the improvement in new algorithms over previously reported results. Having a standard test set |
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and standard evaluation parameters, all based on a resource that provides multiple integrated annotation layers (syntactic |
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parses, semantic roles, word senses, named entities and coreference) and in multiple languages could support joint modeling |
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and help ground and energize ongoing research in the task of entity and event coreference. |
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For more details see https://aclanthology.org/W12-4501.pdf |
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""" |
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class Conll2012Config(datasets.BuilderConfig): |
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"""BuilderConfig for Conll2012""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Conll2012. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Conll2012Config, self).__init__(**kwargs) |
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class Conll2012(datasets.GeneratorBasedBuilder): |
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"""Conll2012 dataset.""" |
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BUILDER_CONFIGS = [ |
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Conll2012Config(name="conll2012", version=datasets.Version("1.0.0"), description="Conll2012 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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'$', |
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"''", |
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'*', |
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',', |
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'-LRB-', |
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'-RRB-', |
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'.', |
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':', |
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'ADD', |
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'AFX', |
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'CC', |
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'CD', |
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'DT', |
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'EX', |
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'FW', |
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'HYPH', |
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'IN', |
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'JJ', |
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'JJR', |
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'JJS', |
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'LS', |
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'MD', |
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'NFP', |
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'NN', |
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'NNP', |
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'NNPS', |
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'NNS', |
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'PDT', |
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'POS', |
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'PRP', |
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'PRP$', |
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'RB', |
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'RBR', |
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'RBS', |
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'RP', |
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'SYM', |
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'TO', |
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'UH', |
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'VB', |
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'VBD', |
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'VBG', |
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'VBN', |
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'VBP', |
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'VBZ', |
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'VERB', |
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'WDT', |
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'WP', |
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'WP$', |
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'WRB', |
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'XX', |
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'``' |
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] |
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) |
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), |
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"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-CARDINAL', |
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'B-DATE', |
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'B-EVENT', |
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'B-FAC', |
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'B-GPE', |
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'B-LANGUAGE', |
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'B-LAW', |
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'B-LOC', |
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'B-MONEY', |
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'B-NORP', |
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'B-ORDINAL', |
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'B-ORG', |
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'B-PERCENT', |
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'B-PERSON', |
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'B-PRODUCT', |
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'B-QUANTITY', |
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'B-TIME', |
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'B-WORK_OF_ART', |
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'I-CARDINAL', |
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'I-DATE', |
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'I-EVENT', |
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'I-FAC', |
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'I-GPE', |
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'I-LANGUAGE', |
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'I-LAW', |
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'I-LOC', |
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'I-MONEY', |
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'I-NORP', |
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'I-ORDINAL', |
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'I-ORG', |
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'I-PERCENT', |
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'I-PERSON', |
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'I-PRODUCT', |
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'I-QUANTITY', |
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'I-TIME', |
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'I-WORK_OF_ART' |
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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://catalog.ldc.upenn.edu/LDC2013T19", |
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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': 'train.txt', |
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'validation': 'validation.txt', |
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'test': 'test.txt', |
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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['validation']}), |
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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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lines = f.readlines() |
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|
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guid = 0 |
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tokens = [] |
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tags = [] |
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pos_tags = [] |
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|
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for line in lines: |
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if line.startswith("-DOCSTART-") or line.strip() == "" 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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'tags': tags, |
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'pos_tags': pos_tags, |
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} |
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guid += 1 |
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tokens = [] |
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tags = [] |
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pos_tags = [] |
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else: |
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|
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splits = line.split('\t') |
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tokens.append(splits[0]) |
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pos_tags.append(splits[1]) |
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tags.append(splits[3].rstrip()) |
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|
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yield guid, { |
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'id': str(guid), |
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'tokens': tokens, |
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'tags': tags, |
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'pos_tags': pos_tags, |
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} |
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