Paper Info
Title
A Time Series Model of the Writing Process.
Abstract
The necessity to operate with the huge number of anonymous documents abounding on the Internet is initiating the study of new methods for authorship recognition. The principal weakness of the methods used in this area is that they assess the similarity of text styles without any regard to their surroundings. This paper proposes a novel mathematical model of the writing process striving to quantify this dependency. A text is divided into a series of sequential sub-documents, which are represented via term histograms. The histograms proximity is estimated through a simple probability distance. Intending to typify the text writing style, a new characteristic representing the mean distance between a current sub-document and numerous earlier ones is advanced. An empirical distribution over the whole document of this feature specifies the writing style. So, dissimilarity of such distributions indicates a difference in the writing styles, and their coincidence implies the styles' identity. Numerical experiments demonstrate high potential ability of the proposed approach.
Year
DOI
Venue
2016
10.1007/978-3-319-41920-6_10
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Learning of internal representations and models,Mining text documents,Text Mining,Time series and sequential pattern mining,Authorship recognition
Histogram,Time series,Empirical distribution function,Computer science,Writing style,Coincidence,Natural language processing,Artificial intelligence,Machine learning,Writing process,The Internet
Conference
Volume
ISSN
Citations 
9729
0302-9743
2
PageRank 
References 
Authors
0.39
16
1
Name
Order
Citations
PageRank
Z. Volkovich17413.19