From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation
|Title||From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||McGregor S, Agres K, Purver M, Wiggins GA|
|Journal||Journal of Artificial General Intelligence|
|Keywords||behavioural validation, computational creativity, concept discovery, conceptual spaces, distributional semantics|
We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.