H.: Learning to rank answers on large online QA collections

This work describes an answer ranking engine for non-factoid questions built using a large online community-generated question-answer collection (Yahoo! Answers). We show how such collections may be used to effectively set up large supervised learning experiments. Furthermore we investigate a wide range of feature types, some exploiting NLP proces-sors, and demonstrate that using them in com-bination leads to considerable improvements in accuracy.