Spatio-Temporal Parsing in Spatial Hypermedia

Abstract

Spatial hypertext represents associations between chunks of information by spatial or visual attributes (such as proximity, color, shape, etc.). This supports expressing information structures implicitly and in an intuitive way. However, automatic recognition of such informal, implicitly encoded structures by a machine (a so-called spatial parser) is still a challenge. Conventional parsers are conceptually restricted by their underlying source of information. Due to this limitation there are various possible structures that cannot be recognized properly, as the machine has no means to detect them. This inevitably limits both the quality of parser output and hence parser performance. In this paper we show that considering temporal aspects in spatial parser design will lead to significant increase in parsing accuracy, detection of richer structures and thus higher parser performance. We call machines that consider such spatial and temporal information spatio-temporal parsers.