Predicting the Quality of Answers Using Surface Linguistic Features
Jung‐Tae Lee, Young-In Song, Hae‐Chang Rim · 2007
Considering the rapidly increasing mass of information on the Web, the quality of documents is a very critical issue in Web information retrieval. This paper presents the importance of surface linguistic features in predicting the quality of user generated documents. A machine learning approach to incorporating surface linguistic features in predicting of document quality is tested on a collection of answers gathered from a community-driven knowledge search service that allows users to ask and answer questions posed by other users. Experimental results show that the features are useful for predicting the quality of answers.