Abbreviating Text Labels on Demand
| dc.contributor.author | Shimabukuro, Mariana | |
| dc.contributor.author | Collins, Christopher | |
| dc.date.accessioned | 2021-02-22T20:04:34Z | |
| dc.date.accessioned | 2022-03-29T20:15:50Z | |
| dc.date.available | 2021-02-22T20:04:34Z | |
| dc.date.available | 2022-03-29T20:15:50Z | |
| dc.date.issued | 2017-10 | |
| dc.description.abstract | Long text labels is a known challenge in information visualizations.There are some techniques used in order to solve this problem like setting a very small font size. On the other hand, sometimes the font size is so small that the text can be difficult to read. Wrapping sentences, dropping letters and text truncation are some techniques do deal with this problem. In order to investigate a solution for labeling long words we ran a study on how people create and interpret word abbreviations. Based on the study data we designed a new algorithm to automatically make words as short as they need to fit the text. Examples applications of this algorithm are presented in this paper. | en |
| dc.description.sponsorship | NSERC Discovery Grants, NSERC Canada Research Chairs | en |
| dc.identifier.citation | M. Shimabukuro and C. Collins, “Abbreviating Text Labels on Demand,” Proc. of IEEE Conf. on Information Visualization (InfoVis Posters), 2017. | en |
| dc.identifier.uri | https://hdl.handle.net/10155/1228 | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.subject | natural language processing | en |
| dc.subject | information visualization | en |
| dc.title | Abbreviating Text Labels on Demand | en |
| dc.type | Article, Research | en |