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Product reviews contain a lot of useful information about product featuresand customer opinions. One important product feature is the complementaryentity (products) that may potentially work together with the reviewed product.Knowing complementary entities of the reviewed product is very importantbecause customers want to buy compatible products and avoid incompatible ones.In this paper, we address the problem of Complementary Entity Recognition(CER). Since no existing method can solve this problem, we first propose anovel unsupervised method to utilize syntactic dependency paths to recognizecomplementary entities. Then we expand category-level domain knowledge aboutcomplementary entities using only a few general seed verbs on a large amount ofunlabeled reviews. The domain knowledge helps the unsupervised method to adaptto different products and greatly improves the precision of the CER task. Theadvantage of the proposed method is that it does not require any labeled datafor training. We conducted experiments on 7 popular products with about 1200reviews in total to demonstrate that the proposed approach is effective.

CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews