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A Framework To Predict The Quality Of Answers With Nontextual, University Of Massachusetts Amherst
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.
Finding Similar Questions In Large Question And Answer Archives, Jiwoon Jeon
Finding Similar Questions In Large Question And Answer Archives, Jiwoon Jeon
Computer Science Department Faculty Publication Series
There has recently been a significant increase in the number of community-based question and answer services on the Web where people answer other peoples’ questions. These services rapidly build up large archives of questions and answers, and these archives are a valuable linguistic resource. One of the major tasks in a question and answer service is to find questions in the archive that a semantically similar to a user’s question. This enables high quality answers from the archive to be retrieved and removes the time lag associated with a community-based system. In this paper, we discuss methods for question retrieval …