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.
@InProceedings{BOUMBER18.535, author = {Dainis Boumber and Yifan Zhang and Arjun Mukherjee}, title = "{Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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