Physical Sciences and Mathematics Commons^™

A Framework To Predict The Quality Of Answers With Nontextual, University Of Massachusetts Amherst

Computer Science Department Faculty Publication Series

New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to pre- dict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a signi¯cant improvement over our baseline.