{
 "id": "SEACrowd/indocoref",
 "owner": "SEACrowd",
 "name": "indocoref",
 "downloads": 50,
 "likes": 0,
 "last_modified": "2024-06-24T13:24:07.000Z",
 "stated_licence": null,
 "stated_from": "none",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [
  "LICENSE"
 ],
 "sources": [],
 "gated": false,
 "tasks": [
  "coreference-resolution"
 ],
 "citation": "@inproceedings{artari-etal-2021-multi,\n    title        = {{A Multi-Pass Sieve Coreference Resolution for Indonesian}},\n    author       = {Artari, Valentina Kania Prameswara  and Mahendra, Rahmad  and Jiwanggi, Meganingrum Arista  and Anggraito, Adityo  and Budi, Indra},\n    year         = 2021,\n    month        = {Sep},\n    booktitle    = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)},\n    publisher    = {INCOMA Ltd.},\n    address      = {Held Online},\n    pages        = {79--85},\n    url          = {https://aclanthology.org/2021.ranlp-1.10},\n    abstract     = {Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74{\\%} of MUC F-measure and 52.18{\\%} of BCUBED F-measure.}\n}\n\n\n@article{lovenia2024seacrowd,\n    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, \n    author",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "6cd36cbc87c28b03aa0f47e173821ebd3326a493",
 "stated_evidence": "https://huggingface.co/datasets/SEACrowd/indocoref/blob/6cd36cbc87c28b03aa0f47e173821ebd3326a493/README.md",
 "file_evidence": "https://huggingface.co/datasets/SEACrowd/indocoref/blob/6cd36cbc87c28b03aa0f47e173821ebd3326a493/LICENSE",
 "card_nc": false
}