Slovene word-sense disambiguation and novelty detection with dictionary examples

Škvorc, Tadej (author), Robnik Šikonja, Marko (author)

Many less-resourced languages struggle with a lack of large, task-specific datasets that are required for solving relevant tasks with modern transformer-based large language models (LLMs). On the other hand, many linguistic resources, such as dictionaries, are rarely used in this context despite their large information contents. We show how LLMs can be used to extend existing language resources in less-resourced languages for two important tasks: word-sense disambiguation (WSD) and threshold-based novelty detection (ND), a task similar to word-sense induction (WSI). We approach the two tasks through the related but much more accessible word-in-context (WiC) task where, given a pair of sentences and a target word, a classification model is tasked with predicting whether the sense of a given word differs between sentences. We demonstrate that a well-trained model for this task can distinguish between different word senses and can be adapted to solve the WSD and ND tasks. The advantage of using the WiC task, instead of directly predicting senses, is that the WiC task does not need pre-constructed sense inventories with a sufficient number of examples for each sense, which are rarely available in less-resourced languages. We show that sentence pairs for the WiC task can be successfully generated from dictionary examples using LLMs. The resulting prediction models outperform existing models on WiC, WSD, and ND tasks. We demonstrate our methodology on the Slovene language, where a monolingual dictionary is available, but word-sense resources are tiny.

• Language: English
• Keywords: large language models, word-sense disambiguation, novelty detection, word-in-context, dictionary examples
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 15 str.
• Numbering: Vol. 166, art. 102638
• PID: 20.500.12556/RUL-187762
• UDC: 004.89:81'322
• ISSN on article: 0169-023X
• DOI: 10.1016/j.datak.2026.102638
• COBISS.SI-ID: 290903811
• Publication date in RUL: 14.09.2026

Record is a part of a journal

• Title: Data & knowledge engineering
• Shortened title: Data knowl. eng.
• Publisher: Elsevier
• ISSN: 0169-023X
• COBISS.SI-ID: 25315584

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: veliki jezikovni modeli, razdvoumljanje pomenov besed, detekcija novih pomenov, beseda-v-kontekstu, slovarski primeri