CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning

TL;DR

This paper introduces a CEFR-annotated WordNet, created using large language models, to better align semantic sense distinctions with language proficiency levels, aiding language learning and NLP tasks.

Contribution

It presents a novel automated method to annotate WordNet with CEFR levels using LLMs, and develops classifiers that perform well on proficiency-level prediction.

Findings

Classifiers achieve a Macro-F1 score of 0.81.

Models fine-tuned on the corpus perform comparably to gold-standard annotations.

Annotated resources are publicly available for NLP and language education.

Abstract

Although WordNet is a valuable resource because of its structured semantic networks and extensive vocabulary, its fine-grained sense distinctions can be challenging for second-language learners. To address this issue, we developed a version of WordNet annotated with the Common European Framework of Reference for Languages (CEFR), integrating its semantic networks with language-proficiency levels. We automated this process using a large language model to measure the semantic similarity between sense definitions in WordNet and entries in the English Vocabulary Profile Online. To validate our approach, we constructed a large-scale corpus containing both sense and CEFR-level information from the annotated WordNet and used it to develop contextual lexical classifiers. Our experiments demonstrate that models fine-tuned on this corpus perform comparably to those fine-tuned on gold-standard annotations. Furthermore, by combining this corpus with the gold-standard data, we developed a practical classifier that achieves a Macro-F1 score of 0.81. This result provides indirect evidence that the transferred labels are largely consistent with the gold-standard levels. The annotated WordNet, corpus, and classifiers are publicly available to help bridge the gap between natural language processing and language education, thereby facilitating more effective and efficient language learning.