Exploring syntactic and semantic features for authorship attribution

Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. In this paper, we propose a novel Multi-Channel Self-Attention Network (MCSAN) incorporating both the inter-channel and inter-positional interaction to extract n-grams of the characters, words, parts of speech (POS), phrase structures, dependency relationships, and topics from multiple dimensions (style, content, syntactic and semantic features) to distinguish different authors. And then we incorporate these extracted features with logistic regression (LR) to do experiments, and the experimental results manifest that our extracted features are effective. Our methods improve 2.1% and 3.0% on CCAT10 and CCAT50, respectively, comparing with state-of-the-art models. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper discusses the importance of authorship attribution and the limitations of existing methods, proposing a novel approach that combines features from multiple dimensions, with experimental results demonstrating its effectiveness compared to state-of-the-art models.