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Abstract Semantic embedding approaches commonly used in natural language processing such as transformer models have rarely been used to examine L2 lexical knowledge. Importantly, their performance has not been contrasted with more traditional annotation approaches to lexical knowledge. This study used NLP techniques related to lexical annotations and semantic embedding approaches to model the receptive vocabulary of L2 learners based on their lexical production during a writing task. The goal of the study is to examine the strengths and weaknesses of both approaches in understanding L2 lexical knowledge. Findings indicate that transformer approaches based on semantic embeddings outperform linguistic annotations and Word2vec models in predicting L2 learners’ vocabulary scores. The findings help to support the strength and accuracy of semantic-embedding approaches as well as their generalizability across tasks when compared to linguistic feature models. Limitations to semantic-embedding approaches, especially interpretability, are discussed.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |