tailieunhanh - Báo cáo khoa học: "Proximity in Context: an empirically grounded computational model of proximity for processing topological spatial expressions∗"

The paper presents a new model for contextdependent interpretation of linguistic expressions about spatial proximity between objects in a natural scene. The paper discusses novel psycholinguistic experimental data that tests and verifies the model. The model has been implemented, and enables a conversational robot to identify objects in a scene through topological spatial relations (. “X near Y”). The model can help motivate the choice between topological and projective prepositions. | Proximity in Context an empirically grounded computational model of proximity for processing topological spatial expressions John D. Kelleher Dublin Institute of Technology Dublin Ireland Geert-Jan M. Kruijff DFKI GmbH SaarbruCken Germany gj@ Fintan J. Costello University College Dublin Dublin Ireland Abstract The paper presents a new model for contextdependent interpretation of linguistic expressions about spatial proximity between objects in a natural scene. The paper discusses novel psycholinguistic experimental data that tests and verifies the model. The model has been implemented and enables a conversational robot to identify objects in a scene through topological spatial relations . X near Y . The model can help motivate the choice between topological and projective prepositions. 1 Introduction Our long-term goal is to develop conversational robots with which we can have natural fluent situated dialog. An inherent aspect of such situated dialog is reference to aspects of the physical environment in which the agents are situated. In this paper we present a computational model which provides a context-dependent analysis of the environment in terms of spatial proximity. We show how we can use this model to ground spatial language that uses topological prepositions the ball near the box to identify objects in a scene. Proximity is ubiquitous in situated dialog but there are deeper cognitive reasons for why we need a context-dependent model of proximity to facilitate fluent dialog with a conversational robot. This has to do with the cognitive load that processing proximity expressions imposes. Consider the examples in 1 . Psycholinguistic data indicates that a spatial proximity expression 1b presents a heavier cognitive load than a referring expression identifying an object purely on physical features 1a yet is easier to process than a projective expression 1c van der Sluis and Krahmer 2004 . The research