Upload dataset
Browse files- README.md +160 -0
- ancora-ca-ner.py +122 -0
- dev.conll +0 -0
- test.conll +0 -0
- train.conll +0 -0
README.md
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---
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languages:
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- ca
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---
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# Named Entites from Ancora Corpus
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## BibTeX citation
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If you use any of these resources (datasets or models) in your work, please cite our latest paper:
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```bibtex
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@inproceedings{armengol-estape-etal-2021-multilingual,
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title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
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author = "Armengol-Estap{\'e}, Jordi and
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Carrino, Casimiro Pio and
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Rodriguez-Penagos, Carlos and
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de Gibert Bonet, Ona and
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Armentano-Oller, Carme and
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Gonzalez-Agirre, Aitor and
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Melero, Maite and
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Villegas, Marta",
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
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month = aug,
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year = "2021",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2021.findings-acl.437",
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doi = "10.18653/v1/2021.findings-acl.437",
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pages = "4933--4946",
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}
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```
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## Digital Object Identifier (DOI) and access to dataset files
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https://doi.org/10.5281/zenodo.4529299
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## Introduction
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This is a dataset for Named Entity Recognition (NER) from <a href="http://clic.ub.edu/corpus/">Ancora corpus</a> adapted for Machine Learning and Language Model evaluation purposes.
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Since multiwords (including Named Entities) in the original Ancora corpus are aggregated as a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") we splitted them to align with word-per-line format, and added conventional <a href="https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)">Begin-Inside-Outside (IOB) tags</a> to mark and classify Named Entities. We did not filter out the different categories of NEs from Ancora (weak and strong). We did 6 minor edits by hand.
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AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence.
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This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
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### Supported Tasks and Leaderboards
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Named Entities Recognition, Language Model
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### Languages
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CA- Catalan
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### Directory structure
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* dev.txt
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* test.txt
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* train.txt
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## Dataset Structure
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### Data Instances
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three two-column files, one for each split.
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### Data Fields
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Every file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one.
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### Example:
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<pre>
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Fundació B-ORG
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Privada I-ORG
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Fira I-ORG
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de I-ORG
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Manresa I-ORG
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ha O
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fet O
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un O
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balanç O
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de O
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l' O
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activitat O
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del O
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Palau B-LOC
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Firal I-LOC
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</pre>
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### Data Splits
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One for each sub-dataset for train, evaluation and test.
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## Dataset Creation
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### Methodology
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We adapted the NER labels from Ancora corpus to a word-per-line format.
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Since multiwords in the original Ancora corpus are aggregated as a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") we splitted them to align with this format, and added conventional <a href="https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)">Begin-Inside-Outside (IOB) tags</a> to mark and classify Named Entities. We did not filter out the different categories of NEs from Ancora (weak and strong). We did 6 minor edits by hand.
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### Curation Rationale
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### Source Data
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#### Initial Data Collection and Normalization
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AnCora consists of a Catalan corpus (AnCora-CA) and a Spanish corpus (AnCora-ES), each of them of 500,000 tokens (some multi-word). The corpora are annotated for linguistic phenomena at different levels.
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AnCora corpus is mainly based on newswire texts. For more information, refer to Taulé, M., M.A. Martí, M. Recasens (2009). “AnCora: Multilevel Annotated Corpora for Catalan and Spanish”, Proceedings of 6th International Conference on language Resources and Evaluation. http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf
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#### Who are the source language producers?
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Catalan Ancora corpus is compiled from articles from the following news outlets: <a href="https://www.efe.com">EFE</a>, <a href="https://www.acn.cat">ACN</a>, <a href="https://www.elperiodico.cat/ca/">El Periodico</a>.
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### Annotations
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#### Annotation process
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We adapted the NER labels from Ancora corpus to a token-per-line, multi-column format.
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#### Who are the annotators?
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Original annotators from Ancora corpus.
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### Dataset Curators
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Carlos Rodríguez and Carme Armentano, from BSC-CNS, did the conversion and curation.
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### Personal and Sensitive Information
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No personal or sensitive information included.
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Contact
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Carlos Rodríguez-Penagos (carlos.rodriguez1@bsc.es) and Carme Armentano-Oller (carme.armentano@bsc.es)
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## License
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<a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Attribution 4.0 International License" style="border-width:0" src="https://chriszabriskie.com/img/cc-by.png" width="100"/></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International License</a>.
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ancora-ca-ner.py
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# Loading script for the Ancora NER dataset.
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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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_DESCRIPTION = """AnCora Catalan NER.
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This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for
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Machine Learning and Language Model evaluation purposes.
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Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as
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a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona")
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we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB)
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tags to mark and classify Named Entites.
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We did not filter out the different categories of NEs from Ancora (weak and strong).
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We did 6 minor edits by hand.
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AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence.
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This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
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"""
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_HOMEPAGE = """https://zenodo.org/record/4762031"""
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_URL = "https://huggingface.co/datasets/bsc/ancora-ca-ner/resolve/main/"
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_TRAINING_FILE = "train.conll"
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_DEV_FILE = "dev.conll"
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_TEST_FILE = "test.conll"
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class AncoraCaNerConfig(datasets.BuilderConfig):
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""" Builder config for the Ancora Ca NER dataset """
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def __init__(self, **kwargs):
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"""BuilderConfig for AncoraCaNer.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(AncoraCaNerConfig, self).__init__(**kwargs)
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class AncoraCaNer(datasets.GeneratorBasedBuilder):
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""" AncoraCaNer dataset."""
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BUILDER_CONFIGS = [
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AncoraCaNerConfig(
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name="AncoraCaNer",
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version=datasets.Version("2.0.0"),
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description="AncoraCaNer dataset"
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),
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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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"ner_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"B-LOC",
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"B-MISC",
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"B-ORG",
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"B-PER",
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"I-LOC",
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"I-MISC",
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"I-ORG",
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"I-PER",
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"O"
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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=_HOMEPAGE,
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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}{_TRAINING_FILE}",
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"dev": f"{_URL}{_DEV_FILE}",
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"test": f"{_URL}{_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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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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"ner_tags": ner_tags,
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}
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guid += 1
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tokens = []
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ner_tags = []
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else:
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# AncoraCaNer tokens are space separated
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splits = line.split('\t')
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tokens.append(splits[0])
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ner_tags.append(splits[1].rstrip())
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# last example
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yield guid, {
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"id": str(guid),
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"tokens": tokens,
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"ner_tags": ner_tags,
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}
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dev.conll
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test.conll
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train.conll
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See raw diff
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