Formalizing Diagnosticity in Concept Combinations

Formalizing Diagnosticity in Concept Combinations Daniela Ferrari (daniela.ferrari@ucd.ie) Department of Philosophy and Social Sciences, University of Siena, Siena, Italy Mark Keane (mark.keane@ucd.ie) Department of Computer Science, University College Dublin, Dublin 4, Ireland Abstract understood by aligning the constituents and mapping the property from one concept to the other. Costello & Keane (2000) propose a constraint-satisfaction theory which sees property and relational compounds as being formed and understood by a unitary mechanism that shapes the meaning using the constraints of diagnosticity, plausibility and informativeness. Costello & Keane have argued that diagnosticity plays a key role in selecting one predicate over another when interpreting all types of compounds. A diagnostic predicate is one that differentiates a concept from all other concepts (for related ideas see Hampton's, 1987, importance; Tversky's, 1977 diagnosticity; Rosch's, 1974, cue validity). So the interpretation of a cactus fish as “a prickly fish” appears to be a more acceptable interpretation than “a green fish” because prickly is more diagnostic of cactus than green. Costello & Keane (1997) confirmed this hypothesis empirically by showing that people rated interpretations using diagnostic properties as being more acceptable than those using non- diagnostic properties. Furthermore, several computational models have shown that diagnosticity operates as an important selection criterion in modelling concept combination (see Costello & Keane, 2000; Lynott, Tagalakis, & Keane, 2004). However, the use of the diagnosticity idea is not without its problems. First, all of the empirical evidence on diagnosticity has only been shown for property compounds not relational ones. Second, and more seriously, it is not at all clear how one could operationally define diagnosticity for relational predicates. People can give ratings for the diagnosticity of the properties prickly and green for cactus but it does not seem sensible to ask people to rate the diagnosticity of operates-at for train. Relational predicates seem to be more external to concepts than properties, in a way that makes them inappropriate for use in direct rating tasks. In this paper, we tackle both of these problems in reverse order. First, we develop a new methodology for characterizing the diagnosticity of all predicates, whether they be property or relational ones, using information retrieval ideas, in particular the tf-idf schema (van Rijsbergen, 1979; see Experiment 1). Second, we report two experiments using these diagnosticity measures to test for their effects in property compounds (Experiment 2) and relational compounds (Experiment 3). Our results indicate that the role A new way to formalize diagnosticity, based on information retrieval ideas, is advanced and then used to examine the effects of diagnosticity on the understanding of novel, noun-noun compounds. Three experiments are reported in which the pattern of diagnosticities on the combination’s constituent concepts is used to predict how the compound will be interpreted. Predictions about how distributions of diagnosticity values affect the number of interpretations produced in property and relational compounds are also tested. The implications of this new methodology and these results for theories of concept combination are discussed. Introduction Conceptual combinations are to language generativity, what fruit flies are to genetics. By examining how people understand noun-noun combinations (e.g. night train, finger cup, and so on) it has been possible to begin to understand the combinatorial richness of human language, a compositional fluency that allows people to create new meanings from previously unseen combinations of known words. One of the key findings in this research area is that there are two main classes of interpretations produced from noun- noun compounds (see e.g., Costello & Keane, 1997; Hampton, 1997; Wisniewski & Gentner, 1991): property interpretations (e.g. when finger cup is interpreted as “a thin cup” by using the property predicate thin from the concept finger) and relational interpretations (e.g., when night train is interpreted using the relational predicate operates-at to connect the two concepts). Indeed, it has been shown that concept combinations tend to be interpreted predominantly in either a property or a relational way, allowing them to be termed property compounds or relational compounds (see Gagne, 2000; Tagalakis & Keane, 2004; Wisniewski, 1996; Wisniewski & Love, 1998). There are several different accounts of how such combinations are understood, that have raised issues about whether relational compounds are more “natural” than property compounds (cf. Gagne, 2000; Gagne & Shoben, 1997). Stressing the importance of distributional knowledge about the relations associated with the compounds constituents, Gagne (2000) has proposed that relational compounds are understood by selecting a relation highly- frequently associated with the modifier (the first word in the compound) to provide an interpretation. Wisniewski and his colleagues have argued that property compounds are