A generic unsupervised method for decomposing multi‐author documents

Akiva, Navot; Koppel, Moshe

doi:10.1002/asi.22924

Cited by 17 publications

(61 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For brevity, in the remainder of this paper, "unsupervised decomposition of a single multi author document" is shortened to "unsupervised authorship decomposition" or "authorship decomposition". The same problem is addressed in (Akiva & Koppel, 2013), except there, the number of authors is assumed known.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

“…BayesAD was empirically compared with AK, a modified version of the approach in (Akiva & Koppel, 2013). The details of AK are contained in Section 5 along with the definition of the accuracy metric used to quantify the comparison.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

“…Si and Sj are put into a cluster if and only if the i th and j th normalized rows of V end up in the same K* means cluster. BayesAD was compared to a baseline approach, denoted "1Author", which assigns a single author to D. BayesAD was also compared to a modified version of the approach in (Akiva & Koppel, 2013), denoted AK and described below.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

“…Let nij denote |Ci∩Γj|. In (Akiva & Koppel, 2013), the accuracy of {C1,…,Cm} was quantified by using purity, a common extrinsic clustering validation index.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

“…Upon request, Navot Akiva provided two corpora which appeared to be the same as ones used in (Akiva & Koppel, 2013): BP Blog, NYT Columnists.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Giannella

2015

Asso for Info Science & Tech

View full text Add to dashboard Cite

Abstract AbstractThis 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 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.

show abstract

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

“…Let nij denote |Ci∩Γj|. In (Akiva & Koppel, 2013), the accuracy of {C1,…,Cm} was quantified by using purity, a common extrinsic clustering validation index.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

“…Upon request, Navot Akiva provided two corpora which appeared to be the same as ones used in (Akiva & Koppel, 2013): BP Blog, NYT Columnists.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Giannella

2015

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

SUDMAD: Sequential and unsupervised decomposition of a multi‐author document based on a hidden markov model

Aldebei

Jia

et al. 2017

Asso for Info Science & Tech

View full text Add to dashboard Cite

Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.

show abstract