Predicate Sense Disambiguation for UMR Annotation of Latin: Challenges and Insights

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492856" target="_blank" >RIV/00216208:11320/24:10492856 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2024.ml4al-1.3/" target="_blank" >https://aclanthology.org/2024.ml4al-1.3/</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Predicate Sense Disambiguation for UMR Annotation of Latin: Challenges and Insights

Original language description

This paper explores the possibility to exploit different Pretrained Language Models (PLMs) to assist in a manual annotation task consisting in assigning the appropriate sense to verbal predicates in a Latin text. Indeed, this represents a crucial step when annotating data according to the Uniform Meaning Representation (UMR) framework, designed to annotate the semantic content of a text in a cross-linguistic perspective. We approach the study as a Word Sense Disambiguation task, with the primary goal of assessing the feasibility of leveraging available resources for Latin to streamline the labor-intensive annotation process. Our methodology revolves around the exploitation of contextual embeddings to compute token similarity, under the assumption that predicates sharing a similar sense would also share their context of occurrence. We discuss our findings, emphasizing applicability and limitations of this approach in the context of Latin, for which the limited amount of available resources poses additi

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GX20-16819X" target="_blank" >GX20-16819X: Language Understanding: from Syntax to Discourse</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 1st Workshop on Machine Learning for Ancient Languages

ISBN

979-8-89176-144-5

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

19-29

Publisher name

Association for Computational Linguistics

Place of publication

Kerrville, TX, USA

Event location

Bangkok, Thailand

Event date

Aug 15, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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