We consider the problem of learning a record matching package (classi\ufb01er) in an active learning setting. In active learning, the learning algorithm picks the set of examples to be labeled, unlike more traditional passive learning setting where a user selects the labeled examples. Active learning is important for record matching since manually identifying a suitable set of labeled examples is dif\ufb01cult. Previous algorithms that use active learning for record matching have serious limitations: The packages that they learn lack quality guarantees and the algorithms do not scale to large input sizes. We present new algorithms for this problem that overcome these limitations. Our algorithms are fundamentally different from traditional active learning approaches, and are designed ground up to exploit problem characteristics speci\ufb01c to record matching. We include a detailed experimental evaluation on realworld data demonstrating the effectiveness of our algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
We consider the problem of learning a record matching package (classi\ufb01er) in an active learning setting. In active learning, the learning algorithm picks the set of examples to be labeled, unlike more traditional passive learning setting where a user selects the labeled examples. 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