Although WordNet is a valuable resource owing to its structured semantic networks and extensive vocabulary, its fine-grained sense distinctions can be challenging for second-language learners. To address this, we developed a 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 method, we constructed a large-scale corpus containing both sense and CEFR-level information from our annotated WordNet and used it to develop contextual lexical classifiers. Our experiments demonstrate that models fine-tuned on our corpus perform comparably to those trained on gold-standard annotations. Furthermore, by combining our corpus with the gold-standard data, we developed a practical classifier that achieves a Macro-F1 score of 0.81, indicating the high accuracy of our annotations. Our 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.

翻译：尽管WordNet凭借其结构化的语义网络和广泛的词汇量成为一种宝贵资源，但其精细的义项区分对第二语言学习者而言可能构成挑战。为解决此问题，我们开发了一个标注有《欧洲语言共同参考框架》（CEFR）的WordNet，将其语义网络与语言熟练度等级相融合。我们利用大型语言模型自动化此过程，以度量WordNet中义项定义与《在线英语词汇大纲》条目之间的语义相似性。为验证我们的方法，我们构建了一个大规模语料库，其中包含来自我们标注版WordNet的义项及CEFR等级信息，并利用该语料库开发了上下文词汇分类器。实验表明，基于我们语料库微调的模型与使用黄金标准标注训练的模型性能相当。此外，通过将我们的语料库与黄金标准数据相结合，我们开发了一个实用的分类器，其宏平均F1分数达到0.81，这表明我们的标注具有高准确性。我们标注的WordNet、语料库及分类器均已公开，旨在弥合自然语言处理与语言教育之间的鸿沟，从而促进更高效的语言学习。