Classification of non-analyzable word types in web documents to implement an effective Korean e-learning system
This work addresses the need for Korean e-learning systems to handle informal language from web documents, but the contribution is incremental as it applies existing grammar graph concepts to a new domain.
The study constructs formal and informal Korean corpora to analyze differences in language use, and proposes Local Grammar Graphs (LGG) to handle non-analyzable word types in informal texts for e-learning systems.
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.