WortsenseDisambiguation

WortsenseDisambiguation is a computational framework for resolving lexical ambiguity by selecting the intended sense of a word in its context. It extends traditional word sense disambiguation (WSD) with emphasis on multilingual applicability and integration with lexical databases.

The architecture typically includes a sense inventory, a context representation module, and a disambiguation model. Approaches combine knowledge-based methods that exploit lexical relations with data-driven methods that learn from annotated corpora or large corpora using contextual embeddings.

Common resources include WordNet, BabelNet, and Open Multilingual WordNet, which provide sense inventories and interlingual mappings. Evaluation uses standard WSD metrics such as precision, recall, and F1 on benchmark datasets from SenseEval and SemEval.

Applications span information retrieval, machine translation, question answering, and semantic search, where accurate sense selection improves interpretation and downstream tasks. WortsenseDisambiguation is closely related to WSD but emphasizes cross-language integration and modular design.

Limitations include uneven sense granularity across languages, data sparsity for low-resource languages, and computational costs for large multilingual models. Ongoing work aims to improve cross-language alignment, domain adaptation, and unsupervised or weakly supervised learning. See also Word Sense Disambiguation, BabelNet, and WordNet for related concepts and resources.