Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5989–6012 | Cite as

Visualize classic play’s composing patterns: a weighted motif mining framework

  • Jiandun LiEmail author
  • Dingyu Yang
  • Pin Lv


Given a corpus of western classic plays, how to efficiently mine their potential composing patterns is an open issue in computational linguistics. Several methods have been proposed to extract and analyze their character networks, however at least two problems remain unsolved: (1) the modeling is time-consuming and imprecise since it is difficult to identify “who talks to whom”, and (2) the analysis fails to reveal the evolving path from micro features to macro emergences, where the key attribute of plays, i.e. diversified protagonist characteristics, shaped. In this paper, by making good use of the play narratage (off screen voice) and the network motif theory, we propose a novel mining framework, called wMotif. The framework consists of five algorithms, preprocessing, stage iteration, character identification, character correlating and weighted motif mining. Considering top 9 referred network indices as contenders, we take 65 real-world classic plays as the dataset and evaluate wMotif’s performance upon a playwright predicting problem. Comparisons show that wMotif is superior in precision, complexity and visualization. Through wMotif we find that, (1) complete triads with pure strong (motif #1306) or weak (motif #1360) edges are the top two significant patterns for playwright predicting, and (2) given a strong speaker-to-listener correlation, whether in most cases both ends loosely connect to a shared character (motif #512) can indicate a work’s genre.


Classic play Computational linguistic Character network Weighted motif 



This work is supported by National Nature Science Foundation of China under grant No.61702320, Shanghai Municipal Education Commission Funds of Teaching Science Research Program No.C17014 and Shanghai Municipal Education Commission Funds of Young Teacher Training Program No.ZZSDJ17021. The authors also thank all anonymous reviewers who greatly help improve the quality of this paper.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronics and InformationShanghai Dianji UniversityShanghaiChina

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