Topic or Style? Exploring the Most Useful Features for Authorship Attribution

By Yunita Sari, Mark Stevenson, Andreas Vlachos. 2018

79 citations recorded by the source corpus.

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Dong Nguyen
2. Thai Le
3. Kai Shu
4. Dongwon Lee
5. Adaku Uchendu
6. Anna Wegmann

Citations in the corpus

• Authorship Attribution for Neural Text Generation (2020, methodology): “Also, interestingly, neural classifiers are able to classify FAIR very accurately unlike classical or existing AA models, while classical Table 7: P3: multi-class classification performance with per-class macro F1 (for each column) and overall average F1 scores of models (for each row). models, especi”
• Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework (2021, methodology): “The following methods are inspired by successful or commonly used approaches in authorship attribution (Neal et al., 2017; Sari et al., 2018).”