We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution.
In REF , Gómez-Adorno et al. showed that textual patterns obtained from shortest path walks over integrated syntactic graphs is a useful methodology for the AA task.
Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Computer Science Medicine