In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of singleword lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language. The word lists used map each word to a single CEFR level, and the task consists of predicting CEFR levels for unseen words. In contrast to previous work on word-level lexical complexity, we experiment with topics as additional features and show that linking words to topics significantly increases accuracy of classification.
REF is another recent study which used CEFR-annotated wordlists SVALex and SweLLex to predict the lexical complexity (i.e. appropriate CEFR level) of single words for learners of Swedish as a second language.
Towards Single Word Lexical Complexity Prediction
BEA@NAACL-HLT
Computer Science