@inproceedings{artari-etal-2021-multi,
title = "A Multi-Pass Sieve Coreference Resolution for {I}ndonesian",
author = "Artari, Valentina Kania Prameswara and
Mahendra, Rahmad and
Jiwanggi, Meganingrum Arista and
Anggraito, Adityo and
Budi, Indra",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.10",
pages = "79--85",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Multi-Pass Sieve Coreference Resolution for Indonesian
%A Artari, Valentina Kania Prameswara
%A Mahendra, Rahmad
%A Jiwanggi, Meganingrum Arista
%A Anggraito, Adityo
%A Budi, Indra
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F artari-etal-2021-multi
%X 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.
%U https://aclanthology.org/2021.ranlp-1.10
%P 79-85
Markdown (Informal)
[A Multi-Pass Sieve Coreference Resolution for Indonesian](https://aclanthology.org/2021.ranlp-1.10) (Artari et al., RANLP 2021)
ACL
- Valentina Kania Prameswara Artari, Rahmad Mahendra, Meganingrum Arista Jiwanggi, Adityo Anggraito, and Indra Budi. 2021. A Multi-Pass Sieve Coreference Resolution for Indonesian. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 79–85, Held Online. INCOMA Ltd..