  Product reviews contain a lot of useful information about product features
and customer opinions. One important product feature is the complementary
entity (products) that may potentially work together with the reviewed product.
Knowing complementary entities of the reviewed product is very important
because 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 a
novel unsupervised method to utilize syntactic dependency paths to recognize
complementary entities. Then we expand category-level domain knowledge about
complementary entities using only a few general seed verbs on a large amount of
unlabeled reviews. The domain knowledge helps the unsupervised method to adapt
to different products and greatly improves the precision of the CER task. The
advantage of the proposed method is that it does not require any labeled data
for training. We conducted experiments on 7 popular products with about 1200
reviews in total to demonstrate that the proposed approach is effective.
