Learning to Rank Answers on Large Online QA Collections
Source:
46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), Columbus, Ohio (2008)
Abstract:
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.
