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URL: http://arxiv.org/pdf/cond-mat/0504165v1

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Authoritative: http://dx.doi.org/10.1142/S021812740701852X   (Publisher's PDF... likely be available here.)

Source: http://arxiv.org/abs/cond-mat/0504165v1

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Abstract

We analyze here a particular kind of linguistic network where vertices represent words and edges stand for syntactic relationships between words. The statistical properties of these networks have been recently studied and various features such as the small-world phenomenon and a scale-free distribution of degrees have been found. Our work focuses on four classes of words: verbs, nouns, adverbs and adjectives. Here, we use spectral methods sorting vertices. We show that the ordering clusters words of the same class. For nouns and verbs, the cluster size distribution clearly follows a power-law distribution that cannot be explained by a null hypothesis. Long-range correlations are found between vertices in the ordering provided by the spectral method. The findings support the use of spectral methods for detecting community structure.

@article{ferrer08spectralClusterWords,
  author={Ramon {Ferrer-i-Cancho} and Andrea Capocci and Guido Caldarelli},
  title={Spectral methods cluster words of the same class in a syntactic dependency network},
  journal={International Journal of Bifurcation and Chaos},
  year={2007},
  month={July},
  volume={17},
  number={7},
  pages={2453-2463},
  doi={10.1142/S021812740701852X},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/ferrer08spectralClusterWords.html}
}