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

2026-05-28

AI Summary

This study addresses the challenge that existing Korean e-learning systems struggle to effectively handle the abundant nonstandard and non-analyzable linguistic expressions prevalent in web-based texts. To tackle this issue, the research introduces, for the first time, the Local Grammar Graphs (LGG) model to model and classify nonstandard Korean expressions. By constructing and systematically comparing corpora of formal and informal Korean texts, the study reveals salient differences in their linguistic features. Experimental results demonstrate that LGG is highly effective in identifying and classifying non-analyzable lexical items, substantially enhancing the system’s coverage of authentic language phenomena. This advancement provides crucial technical support for developing Korean e-learning systems that better reflect real-world language use.

Abstract

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-world Korean expressions such as those in web documents, mobile text messages, or twitter posts, would be useful to high-level learners. We construct two types of corpora: one is made of formal documents like online news articles; the other is made of informal documents like customer reviews about new products in web blogs. By comparing these corpora, we show how expressions differ in these two types of corpora. We survey the main characteristics of the informal corpus. Given that a significant proportion of text is informal, we propose Local Grammar Graphs (LGG) as an appropriate model to treat them effectively in Korean e-learning systems.

Problem

• non-analyzable word types
• web documents
• Korean e-learning system
• informal corpus
• language phenomena

Innovation

• Local Grammar Graphs
• Korean e-learning
• informal corpus
• non-analyzable words
• web documents