Learning to Rank Answers to Why-Questions
Suzan Verberne, Stephan Raaijmakers, D.L. Theijssen, Lou Boves · 2009
The goal of the current research project is to develop a question answering system for answering why-questions (why-QA). Our system is a pipeline consisting of an off-the-shelf retrieval module followed by an answer re-ranking module. In this paper, we aim at improving the ranking performance of our system by finding the optimal approach to learning to rank. More specifically, we try to find the optimal ranking function to be applied to the set of candidate answers in the re-ranking module. We experiment with a number of machine learning algorithms (i.e. genetic algorithms, logistic regression and SVM), with different cost functions. We find that a learning to rank approach using either a regression technique or a genetic algorithm that optimizes for MRR leads to a significant improvement over the TF-IDF baseline. We reach an MRR of 0.341 with a