This paper presents a structured framework for WordNet synset selection applied to Ancient Greek lexical material. Starting from synonym definitions extracted from the Liddell–Scott–Jones (LSJ) lexicon, we compare two strategies: hierarchy-driven aggregation via bounded hypernym trees and LLM-based definitional matching with pairwise ranking. Graded human evaluation shows that structure-aware methods provide a robust baseline, particularly for nouns and verbs, while LLM-based reranking does not consistently improve performance, especially for highly ploysemous groups of synonyms. Beyond supporting the development of an Ancient Greek WordNet, the study highlights the methodological portability of the framework to other languages and lexical resources.

This paper describes the organisation and results of the Named Entity Recognition and Classification (NERC) shared task, conducted as part of EvaLatin 2026. The fourth edition of this evaluation campaign for Natural Language Processing on Latin features two shared tasks, i.e. Dependency Parsing and NERC. After introducing the objective of the task and presenting the Ancient Named Entities Special Interest Group, which aims to address the specific challenges that this task presents, this overview details the annotation tagset, the data provided to the participants and their format. The evaluation metrics and the scorer are also described. Finally, the methodology used by each participating team and their results are presented and discussed.

PREMOVE is a diachronic dataset of Ancient Greek and Latin PREverbed MOtion VErbs, providing manually curated morphological, syntactic, and semantic annotations for almost three thousand verbal occurrences. This paper presents the integration of PREMOVE into the LiLa Knowledge Base of Latin, linking its semantic annotations to WordNet (WN) and VerbNet (VN). We describe the RDF conversion using OntoLex-Lemon and FrAC, enabling explicit modelling of token-level attestations and dataset-level provenance. The resulting linked resource achieves full FAIR compliance and supports complex SPARQL queries, allowing users to explore motion semantics across lexical, textual, and semantic layers. Example SPARQL queries demonstrate how researchers can retrieve attested forms for specific WN synsets or VN classes, supporting reproducible linguistic research and cross-resource exploration of motion semantics in ancient languages.

This paper describes the LiITA Knowledge Base of interoperable linguistic resources for Italian.By adhering to the Linked Open Data principles, LiITA ensures and facilitates interoperability between distributed resources. The paper outlines the lemma-centered architecture of the Knowledge Base and details its core component: the Lemma Bank, a collection of Italian lemmas designed to interlink distributed lexical and textual resources.

The paper introduces the LiIta Knowledge Base of interoperable linguistic resources for Italian. After describing the principles of the Linked Data paradigm, on which LiIta is grounded, the paper presents the lemma-centred architecture of the Knowledge Base and details its core component, consisting of a large collection of Italian lemmas (called the Lemma Bank) used to interlink distributed lexical and textual resources.

This paper describes the process of interlinking a lexical resource consisting of a list of more than 20,000 Neo-Latin words with other resources for Latin. The resources are made interoperable thanks to their linking to the anonymous Knowledge Base, which applies Linguistic Linked Open Data practices and data categories to describe and publish on the Web both textual and lexical resources for the Latin language.