Publications

An automated approach for the recognition of intended messages in grouped bar charts.

Faculty Author(s): Elzer Schwartz, Stephanie
Student Author(s): -
Department: CSCI
Publication: Computational Intelligence
Year: 2019
Abstract: Information graphics (bar charts, line graphs, grouped bar charts, etc) often appear in popular media such as newspapers and magazines. In most cases, the information graphic is intended to convey a high‐level message. This message plays a role in facilitating the discourse purpose of the document but is seldom repeated in the document's text, headlines, or captions. We present a methodology and an implemented system for recognizing the intended message of a grouped bar chart. The recognition system relies on the following components: (1) a linguistic classifier that processes text in the graphic and predicts the most linguistically salient entity from those that are mentioned in text, (2) a cognitive model that estimates the relative perceptual effort required for an individual to recognize some high‐level message in a graph, and (3) a Bayesian network that captures the probabilistic relationship between the high‐level intended message of a graphic and its communicative signals. This research contributes to three applications: accessibility of information graphics for sight‐impaired individuals, retrieval of information graphics from a digital library, and summarization of multimodal documents. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Link: An automated approach for the recognition of intended messages in grouped bar charts.