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Robust stylometric analysis and author attribution based on tones and rimes

Published online by Cambridge University Press:  10 April 2019

Renkui Hou Open the ORCID record for Renkui Hou [Opens in a new window]  and
Chu-Ren Huang
Renkui Hou*
Affiliation:
Department of Linguistics, College of Humanities, Guangzhou University, Guangzhou, China Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Chu-Ren Huang
Affiliation:
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong
*
*Corresponding author. Email: hourk0917@163.com
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Abstract

In this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment-final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes.

Keywords

StylometricsQuantitative stylisticsTone and rime motifsRandom forestSVMAuthor identification
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 1 , January 2020 , pp. 49 - 71
Copyright
© Cambridge University Press 2019 

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