tailieunhanh - Báo cáo khoa học: "Using aggregation for selecting content when generating referring expressions"

Previous algorithms for the generation of referring expressions have been developed specifically for this purpose. Here we introduce an alternative approach based on a fully generic aggregation method also motivated for other generation tasks. We argue that the alternative contributes to a more integrated and uniform approach to content determination in the context of complete noun phrase generation. | Using aggregation for selecting content when generating referring expressions John A. Bateman Sprach- und Literaturwissenschaften University of Bremen Bremen Germany e-mail bateman@ Abstract Previous algorithms for the generation of referring expressions have been developed specifically for this purpose. Here we introduce an alternative approach based on a fully generic aggregation method also motivated for other generation tasks. We argue that the alternative contributes to a more integrated and uniform approach to content determination in the context of complete noun phrase generation. 1 Introduction When generating referring expressions RE it is generally considered necessary to provide sufficient information so that the reader hearer is able to identify the intended referent. A number of broadly related referring expression algorithms have been developed over the past decade based on the natural metaphor of ruling out distractors Reiter 1990 Dale and Haddock 1991 Dale 1992 Dale and Reiter 1995 Horacek 1995 . These special purpose algorithms constitute the standard approach to determining content for RE-generation at this time they have been developed solely for this purpose and have evolved to meet some specialized problems. In particular it was found early on that the most ambitious RE goal that of always providing the maximally concise referring expression necessary for the context full brevity is NP-hard subsequent work on RE-generation has therefore attempted to steer a course between computational tractability and coverage. One common feature of the favored algorithmic simplifications is their in-crementality potential descriptions are successively refined usually non-destructively to produce the final RE which therefore may or may not be minimal. This is also often motivated on grounds of psychological plausibility. In this paper we introduce a completely different metaphor for determining RE-content that may be considered in contrast to or .