Learning Answer Quality Rankings for Non-Factoid Question Answering
Summary: Community QA portals provide an important resource for non-factoid question-answering. The inherent noisiness of user-generated data makes the identification of high-quality content challenging but all the more important. The project presents an approach to answer ranking and shows the usefulness of features that explicitly model answer quality. Furthermore, we introduce the idea of leveraging snippets of web search results for query expansion in answer ranking. We present an evaluation setup that avoids spurious results reported in earlier work. Our results show the usefulness of our features and query expansion techniques, and point to the importance of regularization when learning from noisy data.