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A Two-Phase Shallow Semantic Parsing System Using Clause Boundary Information and Tree Distance  

Park, Kyung-Mi (숭실대학교 컴퓨터학부)
Hwang, Kyu-Baek (숭실대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.5, 2010 , pp. 531-540 More about this Journal
Abstract
In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.
Keywords
Shallow semantic parsing; semantic argument identification; semantic argument classification; maximum entropy models; clause boundary restriction; tree distance restriction;
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