relation-extraction
task1510_evalution_relation_extraction
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1510_evalution_relation_extraction
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1510_evalution_relation_extraction.relation_extractionhttps://e3c.fbk.eu/clinkart#h.t2wonespwj5j
Publications
@inproceedings{evalita2023nermud,
title={{NERMuD} at {EVALITA} 2023: Overview of the Named-Entities Recognition on Multi-Domain Documents Task},
author={Palmero Aprosio, Alessio and Paccosi, Teresa},
booktitle={Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2023)},
publisher = {CEUR.org},
year = {2023},
month =… See the full description on the dataset page: https://huggingface.co/datasets/evalitahf/relation_extraction.Text-Classification-and-Relation-Event-Extraction-Mix-datasetsThe paper of GIELLM dataset.
https://arxiv.org/abs/2311.06838
Cite:
@article{gan2023giellm,
title={Giellm: Japanese general information extraction large language model utilizing mutual reinforcement effect},
author={Gan, Chengguang and Zhang, Qinghao and Mori, Tatsunori},
journal={arXiv preprint arXiv:2311.06838},
year={2023}
}
The dataset constructed base in livedoor news corpus 関口宏司 https://www.rondhuit.com/download.html
English_Relation_Extraction_TaskSlovenian_Relation_ExtractionGreek_Relation_Extraction