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

arXiv: https://arxiv.org/abs/2510.18466 · HTML: https://mlsanity.com/paper/2510.18466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)
- [Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset](https://mlsanity.com/paper/2102.08773/) (2021)
- [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://mlsanity.com/paper/2406.03030/) (2024)
- [TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks](https://mlsanity.com/paper/2403.09207/) (2024)
- [Analysing Zero-Shot Readability-Controlled Sentence Simplification](https://mlsanity.com/paper/2409.20246/) (2024)
- [A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity](https://mlsanity.com/paper/1603.08868/) (2016)
- [Machine Translation using Semantic Web Technologies: A Survey](https://mlsanity.com/paper/1711.09476/) (2017)
- [Exploiting the English Vocabulary Profile for L2 word-level vocabulary assessment with LLMs](https://mlsanity.com/paper/2506.02758/) (2025)
- [Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups](https://mlsanity.com/paper/2411.01706/) (2024)
- [Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages](https://mlsanity.com/paper/1702.01711/) (2017)
- [Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries](https://mlsanity.com/paper/2409.06386/) (2024)

## Abstract (arXiv)

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.