A Compositional and Interpretable Semantic Space

Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion of composition. Our method learns a VSM that is both tailored to support a chosen semantic composition operation, and whose resulting features have an intuitive interpretation. Interpretability allows for the exploration of phrasal semantics, which we leverage to analyze performance on a behavioral task.

Ageing, Justice, and Resource Allocation

Around the world the population is ageing in ways that pose new challenges for health care providers. To date these have mostly been formulated in terms of challenges created by increasing costs, and the focus has been squarely on life prolonging treatments. However, this focus ignores the ways in which many older people require life enhancing treatments to counteract the effects of physical and mental decline. This paper argues that in doing so it misses important aspects of what justice requires when it comes to older people.