Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution

Abstract

We explore the use of Convolutional Neural Networks (CNNs) for multi-label Authorship Attribution (AA) problems and propose a CNN specifically designed for such tasks. By averaging the author probability distributions at sentence level for the longer documents and treating smaller documents as sentences, our multi-label design adapts to single-label datasets and various document sizes, retaining the capabilities of a traditional CNN. As a part of this work, we also create and make available to the public a multi-label Authorship Attribution dataset (MLPA-400), consisting of 400 scientific publications by 20 authors from the field of Machine Learning. Proposed Multi-label CNN is evaluated against a large number of algorithms on MLPA-400 and PAN-2012, a traditional single-label AA benchmark dataset. Experimental results demonstrate that our method outperforms several state-of-the-art models on the proposed task.

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