To address this problem, we propose constructing training examples consisting of one negative exam- ple and one "nearby" positive example.
when agglomerative clustering incorrectly merges two clusters, we select the resulting cluster as the negative example, and select as the positive example a cluster that can be created by merging other existing clusters.