An improved algorithm for unsupervised decomposition of a multi-author document

Chris Giannella

Journal of the Association for Information Science & Technology, 2016, vol. 67, issue 2, 400-411

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This article addresses the problem of unsupervised decomposition of a multi-author text document: identifying the sentences 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 nontrivial 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 levels 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.

Date: 2016
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