Strong correlations between text quality and complex networks features

Concepts of complex networks have been used to obtain metrics that were correlated to text quality established by scores assigned by human judges. Texts produced by high-school students in Portuguese were represented as scale-free networks (word adjacency model), from which typical network features such as the in/outdegree, clustering coefficient and shortest path were obtained. Another metric was derived from the dynamics of the network growth, based on the variation of the number of connected components. The scores assigned by the human judges according to three text quality criteria (coherence and cohesion, adherence to standard writing conventions and theme adequacy/development) were correlated with the network measurements. Text quality for all three criteria was found to decrease with increasing average values of outdegrees, clustering coefficient and deviation from the dynamics of network growth. Among the criteria employed, cohesion and coherence showed the strongest correlation, which probably indicates that the network measurements are able to capture how the text is developed in terms of the concepts represented by the nodes in the networks. Though based on a particular set of texts and specific language, the results presented here point to potential applications in other instances of text analysis.

[1]  Lucas Antiqueira,et al.  Complex networks in the assessment of text quality , 2005 .

[2]  Marcelo A. Montemurro,et al.  Entropic Analysis of the Role of Words in Literary Texts , 2001, Adv. Complex Syst..

[3]  Reinhard Köhler,et al.  Patterns in syntactic dependency networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Ramon Ferrer i Cancho,et al.  The small world of human language , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[5]  S. N. Dorogovtsev,et al.  Evolution of networks , 2001, cond-mat/0106144.

[6]  Joshua B. Tenenbaum,et al.  The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth , 2001, Cogn. Sci..

[7]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[8]  Mariano Sigman,et al.  Global organization of the Wordnet lexicon , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[9]  S N Dorogovtsev,et al.  Language as an evolving word web , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[10]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[11]  Evandro Eduardo Seron Ruiz,et al.  Thesaurus as a complex network , 2004 .

[12]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[13]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[14]  A. Joshi.  Natural Language Processing , 1991, Science.

[15]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[16]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[17]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[18]  Paolo Allegrini,et al.  Cognitive Scale-Free Networks as a Model for Intermittency in Human Natural Language , 2004 .

[19]  Guido Caldarelli,et al.  Spectral Methods Cluster Words of the Same Class in a Syntactic Dependency Network , 2005, Int. J. Bifurc. Chaos.

[20]  O. Kinouchi,et al.  Deterministic Walks in Random Networks: An Application to Thesaurus Graphs , 2001, cond-mat/0110217.

[21]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[22]  S. Shen-Orr,et al.  Superfamilies of Evolved and Designed Networks , 2004, Science.

[23]  A. Barabasi,et al.  Percolation in directed scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Dominic Widdows,et al.  Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination , 2004 .

[25]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

[26]  G. Slater,et al.  A metric to search for relevant words , 2003 .

[27]  Partha Dasgupta,et al.  Topology of the conceptual network of language. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  L. Gonçalves,et al.  Fractal power law in literary English , 2006 .

[29]  G. Caldarelli,et al.  Detecting communities in large networks , 2004, cond-mat/0402499.