AI

Answer typing for information retrieval

Abstract

Answer typing is commonly thought of as finding appropriate responses to given questions. We extend the notion of answer typing to information retrieval to ensure results contain plausible answers to queries. Identification of a large class of applicable queries is performed using a discriminative classifier, and discriminative preference ranking methods are employed for the selection of type-appropriate terms. Experimental results show that type-appropriate terms identified by the model are superior to terms most commonly associated with the query, providing strong evidence that answer typing techniques can find meaningful and appropriate terms. Further experiments show that snippets containing correct answers are ranked higher by our model than by the baseline Google search engine in those instances in which a query does indeed seek a short answer.