Complex networks analysis of language complexity
1 Institute of Physics of São Carlos, University of São Paulo - P. O. Box 369, Postal Code 13560-970, São Carlos, São Paulo, Brazil
2 Institute of Mathematics and Computer Science, University of São Paulo - P. O. Box 369, Postal Code 13560-970, São Carlos, São Paulo, Brazil
Received: 11 September 2012
Accepted: 19 November 2012
Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability.
PACS: 89.75.Hc – Networks and genealogical trees / 02.50.Sk – Multivariate analysis / 89.20.Ff – Computer science and technology
© EPLA, 2012