For a given set of subtopic candidates with annotated subtopics, {(Sn, yn)} (1≤n≤N), we need to estimate the optimal weight w. Empirically, the optimal weight w should minimize the error between the predicted partition y ' and the true partition y, and it should also have a good generalization capability.
these results confirm that the similarity between two subtopic candidates is affected by many factors and our methods can achieve more desirable query subtopics by learning a similarity measure.