  Extracting opinion targets is an important task in sentiment analysis on
product reviews and complementary entities (products) are one important type of
opinion targets that may work together with the reviewed product. In this
paper, we address the problem of Complementary Entity Recognition (CER) as a
supervised sequence labeling with the capability of expanding domain knowledge
as key-value pairs from unlabeled reviews, by automatically learning and
enhancing knowledge-based features. We use Conditional Random Field (CRF) as
the base learner and augment CRF with knowledge-based features (called the
Knowledge-based CRF or KCRF for short). We conduct experiments to show that
KCRF effectively improves the performance of supervised CER task.
