Paper: Semantic Role Labeling Using Maximum Entropy Model

ACL ID W04-2419
Title Semantic Role Labeling Using Maximum Entropy Model
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004
Authors

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