Paper: Learning to Rank Answers on Large Online QA Collections

ACL ID P08-1082
Title Learning to Rank Answers on Large Online QA Collections
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

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.