Using semantic information to answer complex questions

Authors:
Yllias Chali;Sadid A. Hasan;Kaisar Imam
Affiliations:
University of Lethbridge, Lethbridge, AB, Canada;University of Lethbridge, Lethbridge, AB, Canada;University of Lethbridge, Lethbridge, AB, Canada
Venue:
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Year:
2011

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Abstract

In this paper, we propose the use of semantic information for the task of answering complex questions. We use the Extended String Subsequence Kernel (ESSK) to perform similarity measures between sentences in a graph-based random walk framework where semantic information is incorporated by exploiting the word senses. Experimental results on the DUC benchmark datasets prove the effectiveness of our approach.