Marilisa Amoia

Adjective based inference

In this paper, we propose a fine grained classification of english adjectives geared at modeling the distinct inference patterns licensed by each adjective class. We show how it can be implemented in description logic and illustrate the predictions made by a series of examples. The proposal has been implemented using Description logic as a semantic representation language and the prediction verified using the DL theorem prover Racer.

A first order semantic approach to adjectival inference

As shown in the formal semantics literature, adjectives can display very different inferential patterns depending on whether they are intersective, privative, subsective or plain non-subsective. Moreover, many of these classes are often described using second order constructs. In this paper, we adopt Hobbss ontologically promiscuous approach and present a first order treatment of adjective semantics which opens the way for a sophisticated treatment of adjectival inference. The approach was implemented and tested using first order automated reasoners.