Who is the root in a syntactic dependency structure?
Ramon Ferrer-i-Cancho, Marta Arias
TL;DR
The paper investigates the root vertex in syntactic dependency trees through the lens of vertex centrality, testing whether roots are central in the free tree and how positional information aids root discovery. It introduces novel spatial centrality scores (D, D', and coverage) and a soft score based on subtree-size moments, showing that local spatial cues can outperform traditional non-spatial measures in reliably ranking and classifying roots across UD and SUD languages. Empirically, the best results come from the new spatial scores, though language-specific anomalies (notably Japanese in SUD) indicate limits and the need for annotation-aware interpretation. The work provides theoretical and empirical foundations toward a universal notion of rootness in networks and motivates integrating centrality-based root priors into unsupervised parsing pipelines.
Abstract
The syntactic structure of a sentence can be described as a tree that indicates the syntactic relationships between words. In spite of significant progress in unsupervised methods that retrieve the syntactic structure of sentences, guessing the right direction of edges is still a challenge. As in a syntactic dependency structure edges are oriented away from the root, the challenge of guessing the right direction can be reduced to finding an undirected tree and the root. The limited performance of current unsupervised methods demonstrates the lack of a proper understanding of what a root vertex is from first principles. We consider an ensemble of centrality scores, some that only take into account the free tree (non-spatial scores) and others that take into account the position of vertices (spatial scores). We test the hypothesis that the root vertex is an important or central vertex of the syntactic dependency structure. We confirm the hypothesis in the sense that root vertices tend to have high centrality and that vertices of high centrality tend to be roots. The best performance in guessing the root is achieved by novel scores that only take into account the position of a vertex and that of its neighbours. We provide theoretical and empirical foundations towards a universal notion of rootness from a network science perspective.