most citedGenerating training datasets for legal chatbots in Korean

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.CL2026

Classification of non-analyzable word types in web documents to implement an effective Korean e-learning system

Sang-Taek Park, Ae-Lim Ahn, Eric Laporte +1

E-learning systems should deliver contents that reflect various phenomena of the language as it is used. In addition to formal Korean, e-learning systems that would include real-wo…

cs.CL2026

DECO-MWE: building a linguistic resource of Korean multiword expressions for feature-based sentiment analysis

Jaeho Han, Changhoe Hwang, Seongyong Choi +3

This paper aims to construct a linguistic resource of Korean Multiword Expressions for Feature-Based Sentiment Analysis (FBSA): DECO-MWE. Dealing with multiword expressions (MWEs)…

cs.CL2026

Building Korean linguistic resource for NLU data generation of banking app CS dialog system

Jeongwoo Yoon, On-yu Park, Changhoe Hwang +3

Natural language understanding (NLU) is integral to task-oriented dialog systems, but demands a considerable amount of annotated training data to increase the coverage of diverse u…

cs.CL2026

SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis

Suwon Choi, Shinwoo Kim, Changhoe Hwang +3

We report the construction of a Korean evaluation-annotated corpus, hereafter called 'Evaluation Annotated Dataset (EVAD)', and its use in Aspect-Based Sentiment Analysis (ABSA) ex…

cs.CL20261 cited

Generating training datasets for legal chatbots in Korean

Changhoe Hwang, Jee-Sun Nam, Eric Laporte

Chatbots are robots that can communicate with humans using text or voice signals. Legal chatbots improve access to justice, since legal representation and legal advice by lawyers c…