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SEACrowd/indocoref
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- Declared licence
- Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at 6cd36cb
- Licence files
LICENSE, licence not identifiedEvidence: LICENSE at 6cd36cb- Declared sources
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- Access gated on Hugging Face
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Tasks
Citation text from the card
@inproceedings{artari-etal-2021-multi,
title = {{A Multi-Pass Sieve Coreference Resolution for Indonesian}},
author = {Artari, Valentina Kania Prameswara and Mahendra, Rahmad and Jiwanggi, Meganingrum Arista and Anggraito, Adityo and Budi, Indra},
year = 2021,
month = {Sep},
booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)},
publisher = {INCOMA Ltd.},
address = {Held Online},
pages = {79--85},
url = {https://aclanthology.org/2021.ranlp-1.10},
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.}
}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author
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dataset_licence({ "id": "SEACrowd/indocoref" })
signal: "unclear"
stated_licence: null
sources: 0