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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.


Joon-Ho Lim, Young-Sook Hwang, So-Young Park and Hae-Chang Rim, Semantic Role Labeling using Maximum Entropy Model. In: Proceedings of CoNLL-2004, Boston, MA, USA, 2004, pp. 122-125. [ps.gz] [pdf] [bibtex]
Last update: May 13, 2003. erikt@uia.ua.ac.be