A CLP-Based, Diagnosticity-Driven System for Concept Combinations

Diagnosticity operates as an important selection criterion for several computational models of concept combination. Unfortunately, it has not been clear how the diagnosticity of property and relational predicates of the concepts combined can be formalized and quantified. Using an information retrieval method we compute, in a uniform manner, diagnosticity values of concepts predicates. We go on to present a reasoning system that attempts to create meaningful interpretations of novel nounnoun combinations. The system is based solely on diagnostic predicates values and a set of constraint satisfaction rules. We show the effectiveness and plausibility of our methods and discuss their potential.

Conceptual combination with PUNC

Noun-noun compounds play a key role in the growth of language. In this article we present a system for producing and understanding noun-noun compounds (PUNC). PUNC is based on the Constraint theory of conceptual combination and the C-3 model. The new model incorporates the primary constraints of the Constraint theory in an integrated fashion, creating a cognitively plausible mechanism of interpreting noun-noun phrases. It also tries to overcome algorithmic limitations of the C-3 model in being more efficient in its computational complexity, and deal with a wider span of empirical phenomena, such as dimensions of word familiarity. We detail the model, including knowledge representation and interpretation production mechanisms. We show that by integrating the constraints of the Constraint theory of conceptual combination and prioritizing the knowledge available within a concept's representation, PUNC can not only generate interpretations that reflect those produced by people, but also mirror the differences in processing times for understanding familiar, similar and novel word combinations