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SEACrowd/indocoref

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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