The impact of semantic class identification and semantic role labeling on natural language answer extraction

Authors:
Bahadorreza Ofoghi;John Yearwood;Liping Ma
Affiliations:
Centre for Informatics and Applied Optimization, University of Ballarat, Ballarat, VIC, Australia;Centre for Informatics and Applied Optimization, University of Ballarat, Ballarat, VIC, Australia;Centre for Informatics and Applied Optimization, University of Ballarat, Ballarat, VIC, Australia
Venue:
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Year:
2008

Citing 5
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Abstract

In satisfying an information need by a Question Answering (QA) system, there are text understanding approaches which can enhance the performance of final answer extraction. Exploiting the FrameNet lexical resource in this process inspires analysis of the levels of semantic representation in the automated practice where the task of semantic class and role labeling takes place. In this paper, we analyze the impact of different levels of semantic parsing on answer extraction with respect to the individual sub-tasks of frame evocation and frame element assignment.