A Dual-Layer Semantic Role Labeling System

We describe a well-performed semantic role labeling system that further extracts concepts (smaller semantic expressions) from unstructured natural language sentences language independently. A dual-layer semantic role labeling (SRL) system is built using Chinese Treebank and Propbank data. Contextual information is incorporated while labeling the predicate arguments to achieve better performance. Experimental results show that the proposed approach is superior to CoNLL 2009 best systems and comparable to the state of the art with the advantage that it requires no feature engineering process. Concepts are further extracted according to templates formulated by the labeled semantic roles to serve as features in other NLP tasks to provide semantically related cues and potentially help in related research problems. We also show that it is easy to generate a different language version of this system by actually building an English system which performs satisfactory.