Classification of non-analyzable word types in web documents to implement an effective Korean e-learning system
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Classification of non-analyzable word types in web documents to implement an effective Korean e-learning system
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.
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.7.0 |
| Cite as: | arXiv:2605.29638 [cs.CL] |
| (or arXiv:2605.29638v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29638
arXiv-issued DOI via DataCite (pending registration)
|
|
| Journal reference: | Doing Research in Applied Linguistics, 2011, pp. 61-68 |
Access Paper:
- View PDF
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment
May 29
-
What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs
May 29
-
A Modular Architecture for Typologically Controlled Lexicon Generation
May 29
-
MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models
May 29
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.