Automatically scoring metaphor novelty has been largely unexplored, but could be of benefit to a wide variety of NLP applications. We introduce a large, publicly available metaphor novelty dataset to stimulate research in this area, and propose a regression-based approach to automatically score the novelty of potential metaphors that are expressed as word pairs. We additionally investigate which types of features are most useful for this task, and show that our approach outperforms baseline metaphor novelty scoring and standard metaphor detection approaches on this task.

As people age, it is critical that they maintain not only their physical health, but also their cognitive health―for instance, by engaging in cognitive exercise. Recent advancements in AI have uncovered novel ways through which to facilitate such exercise. In this thesis, I propose the first human-robot dialogue system designed specifically to promote cognitive exercise in elderly adults, through discussions about interesting metaphors in books. I describe my work to date, including the development of a new, large corpus and an approach for automatically scoring metaphor novelty. Finally, I outline my plans for incorporating this work into the proposed system.