short-paper

Korean Part-of-speech Tagging Based on Morpheme Generation

Authors:

Seong-Bae ParkAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 19, Issue 3

Article No.: 41, Pages 1 - 10

https://doi.org/10.1145/3373608

Published: 09 January 2020 Publication History

Abstract

Two major problems of Korean part-of-speech (POS) tagging are that the word-spacing unit is not mapped one-to-one to a POS tag and that morphemes should be recovered during POS tagging. Therefore, this article proposes a novel two-step Korean POS tagger that solves the problems. This tagger first generates a sequence of lemmatized and recovered morphemes that can be mapped one-to-one to a POS tag using an encoder-decoder architecture derived from a POS-tagged corpus. Then, the POS tag of each morpheme in the generated sequence is finally determined by a standard sequence labeling method. Since the knowledge for segmenting and recovering morphemes is extracted automatically from a POS-tagged corpus by an encoder-decoder architecture, the POS tagger is constructed without a dictionary nor handcrafted linguistic rules. The experimental results on a standard dataset show that the proposed method outperforms existing POS taggers with its state-of-the-art performance.

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Cited By

Ryu JLim SKwon ONa S(2024)Transformer‐based reranking for improving Korean morphological analysis systemsETRI Journal10.4218/etrij.2023-036446:1(137-153)Online publication date: 28-Feb-2024
https://doi.org/10.4218/etrij.2023-0364
Dalai TMishra TSa P(2024)Deep Learning-based POS Tagger and Chunker for Odia Language Using Pre-trained TransformersACM Transactions on Asian and Low-Resource Language Information Processing10.1145/363787723:2(1-23)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3637877
Park JKim M(2024)Word segmentation granularity in KoreanKorean Linguistics10.1075/kl.00008.par20:1(82-112)Online publication date: 30-May-2024
https://doi.org/10.1075/kl.00008.par
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Index Terms

Korean Part-of-speech Tagging Based on Morpheme Generation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 19, Issue 3

May 2020

228 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3378675

Editor:
Imed Zitouni
Microsoft, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 January 2020

Accepted: 01 November 2019

Revised: 01 July 2019

Received: 01 September 2017

Published in TALLIP Volume 19, Issue 3

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Ministry of Education
Basic Science Research Program through the National Research Foundation of Korea (NRF)

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Cited By

Ryu JLim SKwon ONa S(2024)Transformer‐based reranking for improving Korean morphological analysis systemsETRI Journal10.4218/etrij.2023-036446:1(137-153)Online publication date: 28-Feb-2024
https://doi.org/10.4218/etrij.2023-0364
Dalai TMishra TSa P(2024)Deep Learning-based POS Tagger and Chunker for Odia Language Using Pre-trained TransformersACM Transactions on Asian and Low-Resource Language Information Processing10.1145/363787723:2(1-23)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3637877
Park JKim M(2024)Word segmentation granularity in KoreanKorean Linguistics10.1075/kl.00008.par20:1(82-112)Online publication date: 30-May-2024
https://doi.org/10.1075/kl.00008.par
Ugwu COyewole APopoola OAdetunmbi AElebute A(2024)A part of speech tagger for Yoruba language text using deep neural networkFranklin Open10.1016/j.fraope.2024.1001859(100185)Online publication date: Dec-2024
https://doi.org/10.1016/j.fraope.2024.100185
Yusof MSaee SJuan S(2022)Identifying Relation Between Miriek and Kenyah Badeng Language by Using Morphological Analyzer2022 International Conference on Asian Language Processing (IALP)10.1109/IALP57159.2022.9961253(116-121)Online publication date: 27-Oct-2022
https://doi.org/10.1109/IALP57159.2022.9961253
Warto Muljono Purwanto Noersasongko E(2022)Capitalization Feature and Learning Rate for Improving NER Based on RNN BiLSTM-CRF2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)10.1109/CyberneticsCom55287.2022.9865660(398-403)Online publication date: 16-Jun-2022
https://doi.org/10.1109/CyberneticsCom55287.2022.9865660
Rajani Shree MShambhavi B(2022)POS Tagger Model for South Indian Language Using a Deep Learning ApproachICCCE 202110.1007/978-981-16-7985-8_16(155-167)Online publication date: 16-May-2022
https://doi.org/10.1007/978-981-16-7985-8_16
Jin GYu Z(2021)A Hierarchical Sequence-to-Sequence Model for Korean POS TaggingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/342176220:2(1-13)Online publication date: 23-Apr-2021
https://dl.acm.org/doi/10.1145/3421762

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