Learning Grammar with Explicit Annotations for Subordinating Conjunctions

Abstract

Data-driven approach for parsing may suffer from data sparsity when entirely unsupervised. External knowledge has been shown to be an effective way to alleviate this problem. Subordinating conjunctions impose important constraints on Chinese syntactic structures. This paper proposes a method to develop a grammar with hierarchical category knowledge of subordinating conjunctions as explicit annotations. Firstly, each part-of-speech tag of the subordinating conjunctions is annotated with the most general category in the hierarchical knowledge. Those categories are human-defined to represent distinct syntactic constraints, and provide an appropriate starting point for splitting. Secondly, based on the data-driven state-split approach, we establish a mapping from each automatic refined subcategory to the one in the hierarchical knowledge. Then the data-driven splitting of these categories is restricted by the knowledge to avoid over refinement. Experiments demonstrate that constraining the grammar learning by the hierarchical knowledge improves parsing performance significantly over the baseline.