Document Type
Article
Source Publication Title
The PErvasive Technologies Related to Assistive Environments Conference
DOI
https://dl.acm.org/doi/10.1145/3652037.3663954
Abstract
Hand-crafted features to detect idiolect (individual language style) in short text passages with k-means clustering differentiate individuals based on stylistic indicators. With a relatively small number of input samples and low computational resources, results are comparable to larger-scale methods and are human interpretable. The features make use of common stylometry attributes. An entropybased measure evaluates feature contribution.
Publication Date
6-26-2024
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Koone, Mary E., "Engineered Features for Personal Textual Language Style Clusters" (2024). Association of Computing Machinery Open Access Agreement Publications-Archive. 103.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/103