A Robust Named-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information

被引：3

作者：

Kwon, Sunjae ^{[1
]}

Ko, Youngjoong ^{[2
]}

Seo, Jungyun ^{[3
]}

机构：

[1] Ulsan Natl Inst Sci & Technol, Sch Elec & Comp Engn, Ulsan 44919, South Korea

[2] Dong A Univ, Dept Comp Engn, Busan 49315, South Korea

[3] Sogang Univ, Dept Comp Engn, Seoul 04107, South Korea

来源：

CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT | 2017年

基金：

新加坡国家研究基金会;

关键词：

Korean syllable-level named-entity recognition; Syllable bigram embedding; Eojeol prefix information;

D O I：

10.1145/3132847.3133105

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Korean named-entity recognition (NER) systems have been developed mainly on the morphological-level, and they are commonly based on a pipeline framework that identifies named-entities (NEs) following the morphological analysis. However, this framework can mean that the performance of NER systems is degraded, because errors from the morphological analysis propagate into NER systems. This paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can achieve a similar or better performance compared with the morphological-level NER systems. In addition, because the proposed system does not require a morphological analysis step, its processing speed is about 1.9 times faster than those of the previous morphological-level NER systems.

引用

页码：2139 / 2142

页数：4

共 50 条

[31] A Named Entity Recognition System for Malayalam using Neural Networks
Ajees, A. P.
Idicula, Sumam Mary
8TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2018), 2018, 143 : 962 - 969
[32] CHARACTER-LEVEL EMBEDDING USING FASTTEXT AND LSTM FOR BIOMEDICAL NAMED ENTITY RECOGNITION
Al-Jumaili, Ahmed Sabah Ahmed
Tayyeh, Huda Kadhim
SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2024, 25 (06): : 5258 - 5264
[33] Named Entity Recognition for Code-Mixed Indian Corpus using Meta Embedding
Priyadharshini, Ruba
Chakravarthi, Bharathi Raja
Vegupatti, Mani
McCrae, John P.
2020 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION SYSTEMS (ICACCS), 2020, : 68 - 72
[34] Improving deep learning method for biomedical named entity recognition by using entity definition information
Xiong, Ying
Chen, Shuai
Tang, Buzhou
Chen, Qingcai
Wang, Xiaolong
Yan, Jun
Zhou, Yi
BMC BIOINFORMATICS, 2021, 22 (SUPPL 1)
[35] Improving deep learning method for biomedical named entity recognition by using entity definition information
Ying Xiong
Shuai Chen
Buzhou Tang
Qingcai Chen
Xiaolong Wang
Jun Yan
Yi Zhou
BMC Bioinformatics, 22
[36] Electronic Medical Record Recommendation System Based on Deep Embedding Learning with Named Entity Recognition
Zheng, Yuqian
Yan, Xu
Cao, Xin
Ai, Chunhui
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT VII, 2023, 14260 : 298 - 309
[37] Biomedical named entity recognition using deep neural networks with contextual information
Hyejin Cho
Hyunju Lee
BMC Bioinformatics, 20
[38] Incorporating Global Information into Named Entity Recognition Systems using Relational Context
Merhav, Yuval
Mesquita, Filipe
Barbosa, Denilson
Yee, Wai Gen
Frieder, Ophir
SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, 2010, : 883 - 884
[39] Thai Named-Entity Recognition Using Class-based Language Modeling on Multiple-sized Subword Units
Saykhum, Kwanchiva
Boonpiam, Vataya
Thatphithakkul, Nattanun
Wutiwiwatchai, Chai
Natthee, Cholwich
INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5, 2008, : 1586 - +
[40] Biomedical named entity recognition using deep neural networks with contextual information
Cho, Hyejin
Lee, Hyunju
BMC BIOINFORMATICS, 2019, 20 (01)

← 1 2 3 4 5 →