  Community Question Answering (CQA) platforms contain plenty of CQA texts
(i.e., questions and answers corresponding to the question) where named
entities appear ubiquitously. In this paper, we define a new task of CQA entity
linking (CQAEL) as linking the textual entity mentions detected from CQA texts
with their corresponding entities in a knowledge base. This task can facilitate
many downstream applications including expert finding and knowledge base
enrichment. Traditional entity linking methods mainly focus on linking entities
in news documents, and are suboptimal over this new task of CQAEL since they
cannot effectively leverage various informative auxiliary data involved in the
CQA platform to aid entity linking, such as parallel answers and two types of
meta-data (i.e., topic tags and users). To remedy this crucial issue, we
propose a novel transformer-based framework to effectively harness the
knowledge delivered by different kinds of auxiliary data to promote the linking
performance. We validate the superiority of our framework through extensive
experiments over a newly released CQAEL data set against state-of-the-art
entity linking methods.
