Learning to Generate Diversified Query Interpretations using Biconvex Optimization
Ramakrishna Bairi, A. Ambha, Ganesh Ramakrishnan · International Joint Conference on Natural Language Processing · 2013
The wealth of information present in the World Wide Web has made internet search a de-facto medium for obtaining any required information. Users typically specify short and/or ambiguous queries and expect the answer to appear at the top. Hence, it can be extremely important to produce a diverse but relevant set of results in the precious top k positions. This calls for addressing two types of needs: (i) producing relevant results for queries that are often short and ambiguous and (ii) selecting a set of k diverse results to satisfy different classes of information needs. In this paper, we present a novel technique using a Biconvex optimization formulation as well as adaptations of existing techniques from other areas, for addressing these two problems simultaneously. We propose a graph based iterative method to choose diversified results. We evaluate these approaches on the QRU (Query Representation and Understanding) dataset used in SIGIR 2011 workshop as well as on the AMBIENT (Ambiguous Entities) dataset and present results on generating diversified query interpretations. We also compare these approaches against other online systems such as Surf Canyon, Carrot2, Exalead and DBpedia and empirically demonstrate that our system produces competitive results.