Network analysis of named entity interactions in written texts

The use of methods borrowed from statistics and physics has allowed for the discovery of unprecedent patterns of human behavior and cognition by establishing links between models features and language structure. While current models have been useful to identify patterns via analysis of syntactical and semantical networks, only a few works have probed the relevance of investigating the structure arising from the relationship between relevant entities such as characters, locations and organizations. In this study, we introduce a model that links entities appearing in the same context in order to capture the complexity of entities organization through a networked representation. Computational simulations in books revealed that the proposed model displays interesting topological features, such as short typical shortest path length, high values of clustering coefficient and modular organization. The effectiveness of the our model was verified in a practical pattern recognition task in real networks. When compared with the traditional word adjacency networks, our model displayed optimized results in identifying unknown references in texts. Because the proposed model plays a complementary role in characterizing unstructured documents via topological analysis of named entities, we believe that it could be useful to improve the characterization written texts when combined with other traditional approaches based on statistical and deeper paradigms.