Name
Abstract | ||
|---|---|---|
Authorship attribution is an active research direction due to its legal and financial importance. Its goal is to identify the authorship from the anonymous texts. In this paper, we propose a Topic Drift Model (TDM), which can monitor the dynamicity of authors’ writing styles and learn authors’ interests simultaneously. Unlike previous authorship attribution approaches, our model is sensitive to the temporal information and the ordering of words. Thus it can extract more information from texts. The experimental results show that our model achieves better results than other models in terms of accuracy. We also demonstrate the potential of our model to address the authorship verification problem. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2017.08.022 | Neurocomputing |
Keywords | Field | DocType |
Authorship attribution,Topic model,Topic Drift Model | Writing style,Attribution,Natural language processing,Artificial intelligence,Authorship verification,Topic model,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
273 | C | 0925-2312 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Yang | 1 | 155 | 41.56 |
| Xiaojun Chen | 2 | 1298 | 107.51 |
| Wenting Tu | 3 | 85 | 9.48 |
| Ziyu Lu | 4 | 40 | 7.14 |
| Jia Zhu | 5 | 111 | 18.01 |
| Qiang Qu | 6 | 135 | 12.87 |