Work

Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution

Year: 2018

Type: conference-paper

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.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/4ikgaxhcvh7s

Authors: Dainis Boumber, Yifan Zhang, Arjun Mukherjee

Institution: University of Houston

Language: English

Topic: Authorship Attribution and Profiling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: TWC: Small: Statistical Models for Opinion Spam Detection Leveraging Linguistic and Behavioral Cues