document: 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 into practical use . Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods of using heuristic rules or employing the conventional information retrieval method of Okapi . This is true both when the answers are paragraphs and when they are sentences . Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain , indicating that generic models for definition ranking can be constructed .
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