In definitional question answering (QA), it is essential to rank the candidate answers. In this paper, we propose an online learning algorithm, which dynamically construct the supervisor to reduce the adverse effects of the large number of bad answers and noisy data. We compare our method with two state-of-the-art definitional QA systems and two ranking algorithms, and the experimental results show our method outperforms the others.
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Learning to Rank Answers for Definitional Question Answering
Semantic Scholar · Computer Science · 2015
Abstract
In definitional question answering (QA), it is essential to rank the candidate answers. In this paper, we propose an online learning algorithm, which dynamically construct the supervisor to reduce the adverse effects of the large number of bad answers and noisy data. We compare our method with two state-of-the-art definitional QA systems and two ranking algorithms, and the experimental results show our method outperforms the others.