Tree kernel-based semantic role labeling with enriched parse tree structure

Shallow semantic parsing assigns a simple structure (such as WHO did WHAT to WHOM, WHEN, WHERE, WHY, and HOW) to each predicate in a sentence. It plays a critical role in event-based information extraction and thus is important for deep information processing and management. This paper proposes a tree kernel method for a particular shallow semantic parsing task, called semantic role labeling (SRL), with an enriched parse tree structure. First, a new tree kernel is presented to effectively capture the inherent structured knowledge in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness via considering ancestral information of substructures and approximate matching via allowing insertion/deletion/substitution of tree nodes in the substructures. Second, an enriched parse tree structure is proposed to both well preserve the necessary structured information and effectively avoid noise by differentiating various portions of the parse tree structure. Evaluation on the CoNLL’2005 shared task shows that both the new tree kernel and the enriched parse tree structure contribute much in SRL and our tree kernel method significantly outperforms the state-of-the-art tree kernel methods. Moreover, our tree kernel method is proven rather complementary to the state-of-the-art feature-based methods in that it can better capture structural parse tree information.

Research highlights
► A tree kernel method for semantic role labeling (SRL).
► A new convolution tree kernel with context-sensitiveness and approximate matching.
► An enriched parse tree structure by differentiating various portions of the parse tree.
► The first tree kernel method in SRL, which can compete with feature-based methods.