  Automatic identification of mentioned entities in social media posts
facilitates quick digestion of trending topics and popular opinions.
Nonetheless, this remains a challenging task due to limited context and diverse
name variations. In this paper, we study the problem of entity linking for
Chinese news comments given mentions' spans. We hypothesize that comments often
refer to entities in the corresponding news article, as well as topics
involving the entities. We therefore propose a novel model, XREF, that
leverages attention mechanisms to (1) pinpoint relevant context within
comments, and (2) detect supporting entities from the news article. To improve
training, we make two contributions: (a) we propose a supervised attention loss
in addition to the standard cross entropy, and (b) we develop a weakly
supervised training scheme to utilize the large-scale unlabeled corpus. Two new
datasets in entertainment and product domains are collected and annotated for
experiments. Our proposed method outperforms previous methods on both datasets.
