Learning to rank for why-question answering

In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SV Mmap ) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on Learning to Rank for Information Retrieval. Keywords Learning to Rank · ...

• Type: Journal
• Year: 2011
• Where: IR
• Authors: Suzan Verberne, Hans van Halteren, Daphne Theijssen, Stephan Raaijmakers, Lou Boves
• Keywords: Binary Relevance Data, Hyperparameter Tuning, IR 2011, Machine, Natural Language Processing