Authorship Verification with Entity Coherence and Other Rich Linguistic Features Notebook for PAN at CLEF 2013

We adopt Koppel et al.’s unmasking approach [5] as the major framework of our authorship verification system. We enrich Koppel et al.’s original word frequency features with a novel set of coherence features, derived from our earlier work [2], together with a full set of stylometric features. For texts written in languages other than English, some stylometric features are unavailable due to the lack of appropriate NLP tools, and their coherence features are derived from their translations produced by Google Translate service. Evaluated on the training corpus, we achieve an overall accuracy of 65.7%: 100.0% for both English and Spanish texts, while only 40% for Greek texts; evaluated on the test corpus, we achieve an overall accuracy of 68.2%, and roughly the same performance across three languages.