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.
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Chali, Y., Hasan, S.A., Imam, K. (2011). Using Semantic Information to Answer Complex Questions. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_9
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DOI: https://doi.org/10.1007/978-3-642-21043-3_9
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