This paper investigates the integration of the Linguistic Knowledge Graph (LKG) and Large Language Models (LLMs) for word sense prediction in Latin, a morphologically rich and low-resource historical language. Building on recent work in word sense disambiguation (WSD) and semantic change detection, we use a LKG that integrates information from a diachronic Latin corpus, a sense-annotated dataset of Latin, Latin WordNet, and Wikidata, as a structured representation of semantic and contextual relations. We present sense prediction as a binary classification task over the Latin dataset, using a Graph Retrieval-Augmented Generation approach that combines knowledge graph retrieval with LLM prompting. Two types of graph metadata are tested: author-related information (work, period, occupation) and linguistic metadata (synset and hypernyms derived from WordNet for each word sense). Experiments conducted on GPT-4o-mini, LLaMA-3.1-8B and LLaMA-3.3-70B show varying performance, with F1 scores ranging from 0.53 to 0.77. While GPT-4o-mini achieves the best overall accuracy, LLaMA-3.3-70B benefits the most from graph-based metadata, improving its F1 score by up to 3 points. Analysis by word type reveals that concrete and semantically shifting words are more easily disambiguated than abstract and semantically stable words. Results highlight both the promise and the challenges of combining graph-structured linguistic knowledge with LLMs for historical WSD.

In this submission we propose an approach to encoding etymological information as strings (“etymology strings”). We begin by discussing the advantages of such an approach with respect to one in which etymologies and etymons are explicitly represented as RDF individuals. Next we give a formal description of the regular language underlying our approach as an Extended Backus-Naur Form grammar (EBNF). We use the Chamuça Hindi lexicon as a test case for our approach and show some of the kinds of SPARQL queries which can be made using etymological strings.

This article proposes a linguistic linked open data model for diachronic analysis (LLODIA) that combines data derived from diachronic analysis of multilingual corpora with dictionary-based evidence. A humanities use case was devised as a proof of concept that includes examples in five languages (French, Hebrew, Latin, Lithuanian and Romanian) related to various meanings of the term “revolution” considered at different time intervals. The examples were compiled through diachronic word embedding and dictionary alignment.

Word Sense Disambiguation (WSD) is an important task in NLP, which serves the purpose of automatically disambiguating a polysemous word with its most likely sense in context. Recent studies have advanced the state of the art in this task, but most of the work has been carried out on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open. Although the problem with low-resource languages has recently been mitigated by using existing multilingual resources to propagate otherwise expensive annotations from English to other languages, such techniques have hitherto not been applied to historical languages such as Latin. In this work, we make the following two major contributions. First, we test such a strategy on a historical language and propose a new approach in this framework which makes use of existing bilingual corpora instead of native English datasets. Second, we fine-tune a Latin WSD model on the data produced and achieve state-of-the-art results on a standard benchmark for the task. Finally, we release the dataset generated with our approach, which is the largest dataset for Latin WSD to date. This work opens the door to further research, as our approach can be used for different historical and, generally, under-resourced languages.

We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies. We apply a rule-based system developed for English and a data-driven system trained on Finnish to Swedish and Italian. We find that both systems are accurate enough to bootstrap enhanced dependencies in existing UD treebanks. In the case of Italian, results are even on par with those of a prototype language-specific system.

This paper describes the changes applied to the original process used to convert the Index Thomisticus Treebank, a corpus including texts in Medieval Latin by Thomas Aquinas, into the annotation style of Universal Dependencies. The changes are made both to harmonise the Universal Dependencies version of the Index Thomisticus Treebank with the two other available Latin treebanks and to fix errors and inconsistencies resulting from the original process. The paper details the treatment of different issues in PoS tagging, lemmatisation and assignment of dependency relations. Finally, it assesses the quality of the new conversion process by providing an evaluation against a gold standard.