Korean L2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?

Abstract

Vocabulary knowledge prediction is an important task in lexical text simplification for foreign language learners (L2 learners). However, previously studied methods that use hand-crafted rules based on one or two word features have had limited success. A recent study hypothesized that a supervised learning classifier trained on a large annotated corpus of words unknown by L2 learners may yield better results. Our study crowdsourced the production of such a corpus for Korean, now consisting of 2,385 annotated passages contributed by 357 distinct L2 learners. Our preliminary evaluation of models trained on this corpus show favorable results, thus confirming the hypothesis. In this paper, we describe our methodology for building this resource in detail and analyze its results so that it can be duplicated for other languages. We also present our preliminary evaluation of models trained on this annotated corpus, the best of which recalls 80% of unknown words with 71% precision. We make our annotation data available.

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