Senseattention
Senseattention is an approach in natural language processing that integrates word sense disambiguation with attention mechanisms to produce sense-aware representations of text. The central idea is to treat each token as potentially associated with multiple semantic senses and to learn an attention distribution over these senses conditioned on the surrounding context. The resulting sense-aware representations aim to improve tasks that depend on precise meaning, such as disambiguation, retrieval, and reasoning.
In typical implementations, a token appears with a set of candidate senses drawn from a sense inventory
Training can be supervised, using labeled sense annotations, or weakly supervised via auxiliary objectives (e.g., language
Applications include improving machine translation, information retrieval, question answering, and semantic parsing, especially in domains with
Related concepts include word sense disambiguation, multi-sense embeddings, and attention mechanisms. Challenges involve constructing comprehensive sense