Semantic Role Labeling using Maximum Entropy Model

In this paper, we propose a semantic role labeling method using a maximum entropy model, which enables not only to exploit rich features but also to alleviate the data sparseness problem in a well-founded model. For applying the maximum entropy model to semantic role labeling, we take a incremental approach as follows: firstly, the semantic roles are assigned to the arguments in the immediate clause including a predicate, and then, the semantic roles are assigned to the arguments in the upper clauses by using previously assigned labels. The experimental result shows that the proposed method has about 64.76% (F1-measure) on the test set.