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

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  • Chris Giannella

Abstract

type="main"> 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.

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  • Chris Giannella, 2016. "An improved algorithm for unsupervised decomposition of a multi-author document," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(2), pages 400-411, February.
    • Handle: RePEc:bla:jinfst:v:67:y:2016:i:2:p:400-411
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    File URL: http://hdl.handle.net/10.1002/asi.23375
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