Research of Applying Chain Conditional Random Fields to Semantic Role Labeling

The conditional random fields (CRFs) only can deal with the sequence data of Markov property. And it cannot realize the relationship labeling with more fine structure between semantic roles. An approach to semantic role labeling (SRL) based on chain conditional random fields (CCRFs) model was proposed. The long-distance dependencies between different state variants were handled effectively via labeling hierarchical dependencies and brother dependencies of syntactic dependency tree. Moreover, some new combinative features and prepositional phrase also were added though taking advantages of any features can be added in CRFs model. The experiments were implemented on CoNLL 2008 Shared Task. The results indicate the proposed method can improve precision and recall rate of the system.