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

• URL: http://arxiv.org/abs/2510.18466v1
• Date: Tue, 21 Oct 2025 09:42:48 GMT
• Title: CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning
• Authors: Masato Kikuchi, Masatsugu Ono, Toshioki Soga, Tetsu Tanabe, Tadachika Ozono,
• Abstract summary: We develop a WordNet annotated with the Common European Framework of Reference for Languages (CEFR)<n>We measure the semantic similarity between sense definitions in WordNet and entries in the English Vocabulary Profile Online.<n>Our experiments demonstrate that models fine-tuned on our corpus perform comparably to those trained on gold-standard annotations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysis [0.5545791216381869]
  We explore how agentic large language models (LLMs) can streamline the systematic analysis of annotated corpora.<n>We introduce an agentic framework for corpus-grounded grammatical analysis that integrates concepts such as natural-language task interpretation.<n>We test the system on multilingual grammatical tasks inspired by the World Atlas of Language Structures (WALS)
  arXiv  Detail & Related papers  (2025-11-28T21:27:58Z)
• Autoencoder-Based Framework to Capture Vocabulary Quality in NLP [2.41710192205034]
  We introduce an autoencoder-based framework that uses neural network capacity as a proxy for vocabulary richness, diversity, and complexity.<n>We validate our approach on two distinct datasets: the DIFrauD dataset, which spans multiple domains of deceptive and fraudulent text, and the Project Gutenberg dataset, representing diverse languages, genres, and historical periods.
  arXiv  Detail & Related papers  (2025-02-28T21:45:28Z)
• ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentence [40.4052848203136]
  Implicit language is essential for natural language processing systems to achieve precise text understanding and facilitate natural interactions with users.<n>This paper develops a scalar metric that quantifies the implicitness level of language without relying on external references.<n>We validate ImpScore through a user study that compares its assessments with human evaluations on out-of-distribution data.
  arXiv  Detail & Related papers  (2024-11-07T20:23:29Z)
• A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching [60.51839859852572]
  We propose to resolve the text into multi concepts for multilingual semantic matching to liberate the model from the reliance on NER models. We conduct comprehensive experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
  arXiv  Detail & Related papers  (2024-03-05T13:55:16Z)
• Deep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNets [4.532887563053358]
  This paper introduces two new techniques for automatically propagating sentiment annotations from a partially annotated WordNet to its entirety and to a WordNet in a different language. We evaluated the proposed MSSE+CLDNS method extensively using Princeton WordNet and Polish WordNet, which have many inter-lingual relations. Our results show that the MSSE+CLDNS method outperforms existing propagation methods, indicating its effectiveness in enriching WordNets with emotional metadata across multiple languages.
  arXiv  Detail & Related papers  (2023-12-07T21:44:14Z)
• Prompting Language Models for Linguistic Structure [73.11488464916668]
  We present a structured prompting approach for linguistic structured prediction tasks. We evaluate this approach on part-of-speech tagging, named entity recognition, and sentence chunking. We find that while PLMs contain significant prior knowledge of task labels due to task leakage into the pretraining corpus, structured prompting can also retrieve linguistic structure with arbitrary labels.
  arXiv  Detail & Related papers  (2022-11-15T01:13:39Z)
• Multilingual Word Sense Disambiguation with Unified Sense Representation [55.3061179361177]
  We propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.
  arXiv  Detail & Related papers  (2022-10-14T01:24:03Z)
• Integrating Language Guidance into Vision-based Deep Metric Learning [78.18860829585182]
  We propose to learn metric spaces which encode semantic similarities as embedding space. These spaces should be transferable to classes beyond those seen during training. This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes.
  arXiv  Detail & Related papers  (2022-03-16T11:06:50Z)
• XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization [98.61159823343036]
  We present the Word-in-Context dataset (WiC) for assessing the ability to correctly model distinct meanings of a word. We put forward a large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages. Experimental results show that even when no tagged instances are available for a target language, models trained solely on the English data can attain competitive performance.
  arXiv  Detail & Related papers  (2020-10-13T15:32:00Z)
• Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
  A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size. We propose a fully compositional output embedding layer for language models. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
  arXiv  Detail & Related papers  (2020-09-24T07:21:14Z)
• Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation [5.8523859781812435]
  We show how a state-of-the-art propagation-based model can use it to extend the coverage and quality of its word sense embeddings. We introduce the UWA (Unambiguous Word s) dataset and show how a state-of-the-art propagation-based model can use it to extend the coverage and quality of its word sense embeddings.
  arXiv  Detail & Related papers  (2020-04-29T16:51:21Z)
• Word Sense Disambiguation for 158 Languages using Word Embeddings Only [80.79437083582643]
  Disambiguation of word senses in context is easy for humans, but a major challenge for automatic approaches. We present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings.
  arXiv  Detail & Related papers  (2020-03-14T14:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.