Ranking definitions with supervised learning methods

This paper is concerned with the problem of definition search. Specifically, given a term, we are to retrieve definitional excerpts of the term and rank the extracted excerpts according to their likelihood of being good definitions. This is in contrast to the traditional approaches of either generating a single combined definition or simply outputting all retrieved definitions. Definition ranking is essential for the task. Methods for performing definition ranking are proposed in this paper, which formalize the problem as either classification or ordinal regression. A specification for judging the goodness of a definition is given. We employ SVM as the classification model and Ranking SVM as the ordinal regression model respectively, such that they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined. An enterprise search system based on this method has been developed and has been put i...

• Type: Conference
• Year: 2005
• Where: WWW
• Authors: Jun Xu, Yunbo Cao, Hang Li, Min Zhao
• Keywords: Definition Ranking, Internet Technology, Ordinal Regression, Ranking SVM Models, WWW 2005