Name
Abstract | ||
|---|---|---|
Writing is a fundamental task in our daily life. Existing writing improvement tools mostly focus on low-level grammar error correction, rather than enhancing users' writing styles at the cognitive level. In this work, we present a computational approach that allows learners to have fast but effective learning experience with the help of automatic style transfer, visual stylometry analytics, machine teaching and practice. Our system provides a perfect fusion of vividly visualized style features and principles along with informative examples, which together can shape and drive personalized cognitive learning experience. We demonstrate the effectiveness of our system in a scenario of learning from William Shakespeare.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3290607.3312844 | CHI Extended Abstracts |
Keywords | Field | DocType |
cognitive computing, computational linguistics, machine teaching, visual analytics | Computer science,Writing style,Computational linguistics,Visual analytics,Grammar,Human–computer interaction,Stylometry,Cognition,Analytics,Cognitive computing | Conference |
ISBN | Citations | PageRank |
978-1-4503-5971-9 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaotong Liu | 1 | 91 | 10.11 |
| Anbang Xu | 2 | 351 | 30.52 |
| Zhe Liu | 3 | 50 | 4.87 |
| Yufan Guo | 4 | 162 | 15.45 |
| Rama Akkiraju | 5 | 571 | 49.91 |