<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hristov-2025-large">
    <titleInfo>
        <title>Large Language Models for Lexical Resource Enhancement: Multiple Hypernymy Resolution in WordNet</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dimitar</namePart>
        <namePart type="family">Hristov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Boris</namePart>
            <namePart type="family">Velichkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova-Koleva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Milena</namePart>
            <namePart type="family">Slavcheva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Large language models (LLMs) have materially changed natural language processing (NLP). While LLMs have shifted focus from traditional semantic-based resources, structured linguistic databases such as WordNet remain essential for precise knowledge retrieval, decision making and aiding LLM development. WordNet organizes concepts through synonym sets (synsets) and semantic links but suffers from inconsistencies, including redundant or erroneous relations. This paper investigates an approach using LLMs to aid the refinement of structured language resources, specifically WordNet, by an automation for multiple hypernymy resolution, leveraging the LLMs semantic knowledge to produce tools for aiding and evaluating manual resource improvement.</abstract>
    <identifier type="citekey">hristov-2025-large</identifier>
    <location>
        <url>https://aclanthology.org/2025.ranlp-stud.3/</url>
    </location>
    <part>
        <date>2025-09</date>
        <extent unit="page">
            <start>20</start>
            <end>26</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Large Language Models for Lexical Resource Enhancement: Multiple Hypernymy Resolution in WordNet
%A Hristov, Dimitar
%Y Velichkov, Boris
%Y Nikolova-Koleva, Ivelina
%Y Slavcheva, Milena
%S Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F hristov-2025-large
%X Large language models (LLMs) have materially changed natural language processing (NLP). While LLMs have shifted focus from traditional semantic-based resources, structured linguistic databases such as WordNet remain essential for precise knowledge retrieval, decision making and aiding LLM development. WordNet organizes concepts through synonym sets (synsets) and semantic links but suffers from inconsistencies, including redundant or erroneous relations. This paper investigates an approach using LLMs to aid the refinement of structured language resources, specifically WordNet, by an automation for multiple hypernymy resolution, leveraging the LLMs semantic knowledge to produce tools for aiding and evaluating manual resource improvement.
%U https://aclanthology.org/2025.ranlp-stud.3/
%P 20-26

Markdown (Informal)

[Large Language Models for Lexical Resource Enhancement: Multiple Hypernymy Resolution in WordNet](https://aclanthology.org/2025.ranlp-stud.3/) (Hristov, RANLP 2025)

• Large Language Models for Lexical Resource Enhancement: Multiple Hypernymy Resolution in WordNet (Hristov, RANLP 2025)

ACL

• Dimitar Hristov. 2025. Large Language Models for Lexical Resource Enhancement: Multiple Hypernymy Resolution in WordNet. In Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing, pages 20–26, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.