ABSTRACT
Advances in computer technology have revolutionized the ways in which linguists can approach their data. By accessing large bodies of digital text (corpora) and searching for linguistic patterns and frequency information related to phenomena of interest, we can uncover complexities in naturally occurring data and explore broader issues. Although corpus-based research in Korean linguistics has surged over the past several decades, there has not been much corpus-based work in Korean as a second language (KSL). However, in line with growing academic interests in combining technology in language learning and teaching, the necessity of learner corpora and annotation has been recognized as a primary subject in second language acquisition (SLA) research. Learner corpora include electronic collections of written or spoken data produced by language learners. Granger emphasizes the interdependency of SLA and learner corpora; SLA provides cognitive and psychological platforms for analyzing learner language, and learner corpora broaden SLA by providing enriched methods for learner data analysis and developing pedagogical tools. This paper introduces diverse Korean learner corpora resources and explores empirical applications of learner corpora in KLS. The design of learner corpora and annotation methodology will be explained in conjunction with demonstrating the usability of annotated learner corpora. The paper provides comprehensive knowledge on compiling a learner corpus and adding linguistic markups. It emphasizes practical insights into utilizing learner data; extracting meaningful information from patterns, errors, and interlanguage; and understanding pedagogical implications to embark on learner corpus research in Korean.
