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Beyond Vectors: Subspace Representations for Set Operations of Embeddings
In natural language processing (NLP), the role of embeddings in representing
linguistic semantics is crucial. Despite the prevalence of vector
representations in embedding sets, they exhibit limitations in expressiveness
and lack comprehensive set operations. To address this, we attempt to formulate
and apply sets and their operations within pre-trained embedding spaces.
Inspired by quantum logic, we propose to go beyond the conventional vector set
representation with our novel subspace-based approach. This methodology
constructs subspaces using pre-trained embedding sets, effectively preserving
semantic nuances previously overlooked, and consequently consistently improving
performance in downstream tasks
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