This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidden Markov Model ( HMM ) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships . An unsupervised learning method is developed to initialize the HMM parameters . Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests . As an example of its applications , the proposed approach is applied for attributing authorship of a document and has also shown promising results . Authorship analysis is a process of inspecting documents in order to extract authorial information about these documents . It is considered as a general concept that embraces several types of authorship subjects , including authorship verif ication , plagiarism detection and author attribution . Authorship verification ( Brocardo et al . , 2013 ; Potha and Stamatatos , 2014 ) decides whether a given document is written by a specific author . Plagiarism detection ( Stein et al . , 2011 ; Kestemont et al . , 2011 ) seeks to expose the similarity between two texts . However , it is un-able to determine if they are written by the same author . In author attribution ( Juola , 2006 ; Savoy , 2015 ) , a real author of an anonymous document is predicted using labeled documents of a set of candidate authors . Another significant subject in authorship analysis , which has received comparatively less attention from research community , is authorship-based document decomposition ( ABDD ) . This subject is to group the sentences of a multi-author document to different classes , of which each contains the sentences written by only one author . Many applications can take advantage of such a subject , especially those in forensic investigation , which aim to determine the authorship of sentences in a multi-author document . Furthermore , this kind of subject is beneficial for detecting plagiarism in a document and defining contributions of authors in a multi-author document for commercial purpose . ABDD can also be applied to identify which source ( regarded as an ' author ' in this paper ) a part of a document is copied from when the document is formed by taking contents from various sources . In despite of the benefits of ABDD , there has been little research reported on this subject . Koppel et al . ( 2011 ) are the first researchers who implemented an unsupervised approach for ABDD . However , their approach is restricted to Hebrew documents only . The authors of Akiva and Koppel ( 2013 ) addressed the drawbacks of the above approach by proposing a generic unsupervised approach for ABDD . Their approach utilized distance measurements to increase the precision and accuracy of clustering and classification phases , respectively . The accuracy of their approach is highly dependent on the number of au-thors . When the number of authors increases , the accuracy of the approach is significantly dropped . Giannella ( 2015 ) presented an improved approach for ABDD when the number of authors of the document is known or unknown . In his approach , a Bayesian segmentation algorithm is applied , which is followed by a segment clustering algorithm . However , the author tested his approach by using only documents with a few transitions among authors . Furthermore , the accuracy of the approach is very sensitive to the setting of its parameters . In Aldebei et al . ( 2015 ) , the authors presented an unsupervised approach ABDD by exploiting the differences in the posterior probabilities of a Naive-Bayesian model in order to increase the precision and the classification accuracy , and to be less dependent on the number of authors in comparing with the approach in Akiva and Koppel ( 2013 ) . Their work was tested on documents with up to 400 transitions among authors and the accuracy of their approach was not sensitive to the setting of parameters , in contrast with the approach in Giannella ( 2015 ) . However , the performance of their approach greatly depends on a threshold , of which the optimal value for an individual document is not easy to find . Some other works have focused on segmenting a document into components according to their topics . For applications where the topics of documents are unavailable , these topicbased solutions will fail . In this paper , the ABDD approach is independent of documents ' topics . All of the existing works have assumed that the observations ( i.e. , sentences ) are independent and identically distributed ( i.i.d . ) . No consideration has been given to the contextual information between the observations . However , in some cases , the i.i.d . assumption is deemed as a poor one ( Rogovschi et al . , 2010 ) . In this paper , we will relax this assumption and consider sentences of a document as a sequence of observations . We make use of the contextual information hidden between sentences in order to identify the authorship of each sentence in a document . In other words , the authorships of the " previous " and " subsequent " sentences have relationships with the authorship of the current sentence . There-fore , in this paper , a well-known sequential model , Hidden Markov Model ( HMM ) , is used for modelling the sequential patterns of the document in order to describe the authorship relationships . The contributions of this article are summarized as follows . 1 . We capture the dependencies between consecutive elements in a document to identify different authorial components and construct an HMM for classification . It is for the first time the sequential patterns hidden among document elements is considered for such a problem . 2 . To build and learn the HMM model , an unsupervised learning method is first proposed to estimate its initial parameters , and it does not require any information of authors or document 's context other than how many authors have contributed to write the document . 3 . Different from the approach in Aldebei et al . ( 2015 ) , the proposed unsupervised approach no longer relies on any predetermined threshold for ABDD . 4 . Comprehensive experiments are conducted to demonstrate the superior performance of our ideas on both widely-used artificial benchmark datasets and an authentic scientific document . As an example of its applications , the proposed approach is also applied for attributing authorship on a popular dataset . The proposed approach can not only correctly determine the author of a disputed document but also provide a way for measuring the confidence level of the authorship decision for the first time . The rest of this article is organised as follows . Section 2 reviews the HMM . Section 3 presents the details of our proposed approach , including the processes for initialization and learning of HMM parameters , and the Viterbi decoding process for classification . Experiments are conducted in Section 4 , followed by the conclusion in Section 5 . We have developed an unsupervised approach for decomposing a multi-author document based on authorship . Different from the stateof-the-art approaches , we have innovatively made use of the sequential information hidden among document elements . For this purpose , we have used HMM and constructed a sequential probabilistic model , which is used to find the best sequence of authors that represents the sentences of the document . An unsupervised learning method has also been developed to estimate the initial parameter values of HMM . Comparative experiments conducted on benchmark datasets have demonstrated the effectiveness of our ideas with superior perfor-mance achieved on both artificial and authentic documents . An application of the proposed approach on authorship attribution has also achieved perfect results of 100 % accuracies together with confidence measurement for the first time .
There is no method for multiauthor segmentation of a document into author components which can be applied to authorship verification, plagiarism detection and author attribution.
They propose a HMM-based sequential probabilistic model that captures the dependencies of sequential sentences and their authors coupled with an unsupervised initialization method.
Experiments with artificial and authentic scientific document datasets show that the proposed model outperforms existing methods and also be able to provide confidence scores.