Learning to Rank Answers on Large Online QA Collections
Mihai Surdeanu, Massimiliano Ciaramita, Hugo Zaragoza · 2008
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 processors, and demonstrate that using them in combination leads to considerable improvements in accuracy. 1