Authorship analysis is a field of study which aims to infer authorship information from a document or group of documents. This paper develops an approach, BayesAD, for solving the authorship decomposition problem when there are multiple authors involved.
An Improved Algorithm for Unsupervised Decomposition of a Multi-Author Document
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This paper addresses the problem of unsupervised decomposition of a multi-author text document: identifying the sentences that were written by each author assuming the number of authors is unknown. An approach, BayesAD, is developed for solving this problem: apply a Bayesian segmentation algorithm, followed by a segment-clustering algorithm. Results are presented from an empirical comparison between BayesAD and AK, a modified version of an approach published by Akiva and Koppel in 2013.
BayesAD exhibited greater accuracy than AK in all experiments. However, BayesAD has a parameter that needs to be set and which had a non-trivial impact on accuracy. Developing an effective method for eliminating this need would be a fruitful direction for future work. When controlling for topic, the accuracy of BayesAD and AK were, in all but one case, worse than a baseline approach wherein one author was assumed to write all sentences in the input text document. Hence, room for improved solutions exists.