Transformer‐based reranking for improving Korean morphological analysis systems

Author:

Ryu Jihee12ORCID,Lim Soojong1,Kwon Oh‐Woog1,Na Seung‐Hoon2ORCID

Affiliation:

1. Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea

2. Division of Computer Science and Engineering Jeonbuk National University Jeonju Republic of Korea

Abstract

AbstractThis study introduces a new approach in Korean morphological analysis combining dictionary‐based techniques with Transformer‐based deep learning models. The key innovation is the use of a BERT‐based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first‐stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary‐based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.

Funder

Institute for Information and Communications Technology Promotion

Publisher

Wiley

Reference43 articles.

1. T.Mikolov K.Chen G.Corrado andJ.Dean Efficient estimation of word representations in vector space (1st International Conference on Learning Representations ICLR 2013 Scottsdale Arizona USA) May 2‐4 2013 2013.

2. Subword tokenization and Korean morphological analysis;Song H.‐J.;Commun. KIISE,2021

3. Performance Analysis of Korean Morphological Analyzer based on Transformer and BERT

4. E.ChungandJ.‐G.Park Word segmentation and POS tagging using Seq2seq attention model (Proceedings of the 28th Annual Conference on Human and Cognitive Language Technology) 2016 pp.217–219.

5. H.HwangandC.Lee Korean morphological analysis using sequence‐to‐sequence learning with copying mechanism (Proceedings of the 43rd Winter Congress of the KIISE) 2016 pp.443–445.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3