  In this paper, we propose CHOLAN, a modular approach to target end-to-end
entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two
transformer-based models integrated sequentially to accomplish the EL task. The
first transformer model identifies surface forms (entity mentions) in a given
text. For each mention, a second transformer model is employed to classify the
target entity among a predefined candidates list. The latter transformer is fed
by an enriched context captured from the sentence (i.e. local context), and
entity description gained from Wikipedia. Such external contexts have not been
used in the state of the art EL approaches. Our empirical study was conducted
on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical
results suggest that CHOLAN outperforms state-of-the-art approaches on standard
datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.

Computation and Language