A Multi-Pass Sieve Coreference Resolution for Indonesian

Valentina Kania Prameswara Artari, Rahmad Mahendra, Meganingrum Arista Jiwanggi, Adityo Anggraito, Indra Budi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 examines the portability of multi-pass sieve coreference resolution model to Indonesian language. We conduct the experiment on 201 Wikipedia documents and multi-pass sieve system yields 72.74% of MUC F-measure and 52.18% of BCUBED F-measure.

Original languageEnglish
Title of host publicationInternational Conference Recent Advances in Natural Language Processing, RANLP 2021
Subtitle of host publicationDeep Learning for Natural Language Processing Methods and Applications - Proceedings
EditorsGalia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva
PublisherIncoma Ltd
Pages79-85
Number of pages7
ISBN (Electronic)9789544520724
DOIs
Publication statusPublished - 2021
EventInternational Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021 - Virtual, Online
Duration: 1 Sep 20213 Sep 2021

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
ISSN (Print)1313-8502

Conference

ConferenceInternational Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021
CityVirtual, Online
Period1/09/213/09/21

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