A corpus-based study on synesthesia in Korean ordinary language
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Grammatical Error Annotation for Korean Learners of Spoken English
The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus. We collected raw story-telling speech from Korean university students. Transcrip…
Grammatical Error DetectionDoes Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers
Syntactic elements, such as word order and case markers, are fundamental in natural language processing. Recent studies show that syntactic information boosts language model performance and offers clues for people to und…
Data AugmentationLanguage ModelingLanguage ModellingSentenceBuilding Korean Abstract Meaning Representation Corpus
To explore the potential sembanking in Korean and ways to represent the meaning of Korean sentences, this paper reports on the process of applying Abstract Meaning Representation to Korean, a semantic representation fram…
Abstract Meaning RepresentationLearning How to Translate North Korean through South Korean
South and North Korea both use the Korean language. However, Korean NLP research has focused on South Korean only, and existing NLP systems of the Korean language, such as neural machine translation (NMT) models, cannot …
Machine TranslationNMTTranslationEnriching the Korean Learner Corpus with Multi-reference Annotations and Rubric-Based Scoring
Despite growing global interest in Korean language education, there remains a significant lack of learner corpora tailored to Korean L2 writing. To address this gap, we enhance the KoLLA Korean learner corpus by adding m…
DiversityGrammatical Error Correction