One Distributional Memory, Many Semantic Spaces

We propose an approach to corpus-based semantics, inspired by cognitive science, in which different semantic tasks are tackled using the same underlying repository of distributional information, collected once and for all from the source corpus. Task-specific semantic spaces are then built on demand from the repository. A straightforward implementation of our proposal achieves state-of-the-art performance on a number of unrelated tasks.

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