TL;DR
The proposed k-Multilingual Concept model allows to uncover novel layers of lexical knowledge in the form of multifaceted conceptual links between naturally disambiguated sets of words.
Word Sense Disambiguation has advanced rapidly for English and a handful of well-resourced modern languages, but it continues to assume the existence of a sense inventory and a word-to-sense mapping in the source language (Navigli, 2026). These assumptions break down for most historical and low-resource languages, whose dedicated WordNets are either incomplete or still under construction. We present Inspicio, an open-vocabulary retrieval pipeline that links tokens in context to synsets of the Open English WordNet (McCrae et al., 2020) without requiring any source-language inventory or mapping. For each occurrence, an instruction-tuned LLM produces two English translations of the surrounding sentence, a small set of candidate dictionary-style definitions, and a few candidate English lemmas. These outputs drive a hybrid retrieval step that combines dense definition-synset similarity, sparse lemma matching, and Maximal Marginal Relevance re-ranking. We evaluate the pipeline across a 6x6 grid of LLMs and sentence-embedding models on a new bilingual set of manually annotated Latin and Ancient Greek perception verbs, on a subset of PREMOVE dataset (Farina, 2025), and on a diachronic sample of Italian. The best configuration reaches 96% Recall@50 on the perception-verb test set, with each component contributing measurable gains, and remains competitive in the out-of-domain and cross-lingual settings.
Results show that contextual transformer representations are quite successful for low-resource WSD, although there is still a significant class imbalance that limits performance.
Word Sense Disambiguation (WSD) remains a challenge in natural language processing, particularly for morphologically rich and less-resourced languages, e.g., Serbian, where complex inflectional systems intensify lexical ambiguity. Existing WSD approaches often struggle because of scarce annotated corpora, inadequate sense inventories, and linguistically complex languages. This paper presents a semi-automated framework for Serbian WSD resource development that employs Large Language Models (LLMs) and a constrained JSON-formatted output. The approach combines LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation, with the aim of supporting the practical construction and refinement of sense-annotated resources in a low-resource setting. In WSD experiments, LLMs significantly outperformed the Serbian-specific TeslaXLM , the multilingual embedding model intfloat/multilingual-e5-large , and the sentence transformer all-MiniLM-L6-v2 . The expanded sense inventory improved the accuracy, particularly for GPT-4.1 , which achieved 92.4% for single-word and 95.2% for multi-word spans. These results position LLMs as potentially practical for scalable sense-annotated data creation in less-resourced settings. The proposed methodology addresses key limitations in existing WSD pipelines for Serbian, and contributes to the broader effort of adapting LLMs for semantic processing in linguistically diverse and data-scarce environments.
This work proposes GPTKB 2.0, a methodology for constructing disambiguated KBs directly from large language models (LLMs) that incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy.
Multilingual Question Answering (MQA) is primarily characterized by semantic-based and symbolic-based approaches, remain constrained by Language Confinement. Semantic methods, such as RAG and Agentic frameworks, suffer from linguistic bias that restricts retrieval and reasoning to the source language, while symbolic approaches struggle with cross-lingual schema alignment. We consider that integrating multilingual information from the semantic space into the symbolic space to formulate precise queries enables the effective utilization of global multilingual knowledge. In this paper, we propose SeSyCo, a Semantic-Symbolic Knowledge Consensus framework. Specifically, we leverage the semantic space to diverge monolingual queries into broad multilingual evidence, and subsequently utilize the symbolic space to eliminate language discrepancies, converging the gathered information into a robust consensus for precise SPARQL generation. Extensive experiments on the MLaKE dataset demonstrate that SeSyCo outperforms the strongest baseline by 10.7% in multi-hop settings, validating that establishing a robust multilingual consensus is essential for enhancing MQA performance. The collection is available at https://github.com/YuZhang9408/SeSyCo.
Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
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