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.
Inspicio is presented, 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.
Results show that contextual transformer representations are quite successful for low-resource WSD, although there is still a significant class imbalance that limits performance.
LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation is combined with a constrained JSON-formatted output to support the practical construction and refinement of sense-annotated resources in a low-resource setting.
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.
This paper proposes SeSyCo, a Semantic-Symbolic Knowledge Consensus framework, which leverages 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...
The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as a mapping problem preserving distributional properties, an optimization problem minimizing loss through gradient-based methods, and an evaluation problem assessing quality through semantic simila...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
