Earticle

초록

영어

This study proposes a hybrid model for accurate Korean named entity recognition and sentiment analysis within the tourism domain. To address the limitations of existing research due to the lack of domain-specific language models, we collaborated with industry partners to collect and construct tourism-specialized textual datasets. The resulting AI-Hub tourism corpus, developed through this data acquisition process, was utilized for model development and performance evaluation. Our approach combines the pre-trained KoELECTRA model with a Bidirectional Gated Recurrent Unit (BiGRU) architecture. KoELECTRA leverages the strengths of Transformer-based contextual representations, while BiGRU enhances contextual coherence by processing bidirectional sequential information. Evaluation results confirm that the proposed hybrid model demonstrates effective applicability for natural language processing tasks in the Korean tourism domain.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. TRANSFER LEARNING-BASED KOREAN TOKEN TAGGING MODEL
A. Challenges in Korean Token Tagging
B. Proposed Model Architecture
C. Training Strategy
IV. EXPERIMENTS
A. Experimental Setup
B. Experimental Results
REFERENCES

저자

• Hong Jo AN [ Dept. of AI Developer RNA Analytics Seoul, Republic of Korea ]
• Gyu Tae Park [ Dept. of Computer Science & Engineering Kyungnam University Changwon, Republic of Korea ]
• Woo Jin Sim [ Dept. of Computer Science & Engineering Kyungnam University Changwon, Republic of Korea ]
• Byung-Joo Shin [ Dept. of Computer Science & Engineering Kyungnam University Changwon, Republic of Korea ] Corresponding Author

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