  Semantic entity recognition is an important task in the field of
visually-rich document understanding. It distinguishes the semantic types of
text by analyzing the position relationship between text nodes and the relation
between text content. The existing document understanding models mainly focus
on entity categories while ignoring the extraction of entity boundaries. We
build a novel hypergraph attention document semantic entity recognition
framework, HGA, which uses hypergraph attention to focus on entity boundaries
and entity categories at the same time. It can conduct a more detailed analysis
of the document text representation analyzed by the upstream model and achieves
a better performance of semantic information. We apply this method on the basis
of GraphLayoutLM to construct a new semantic entity recognition model
HGALayoutLM. Our experiment results on FUNSD, CORD, XFUND and SROIE show that
our method can effectively improve the performance of semantic entity
recognition tasks based on the original model. The results of HGALayoutLM on
FUNSD and XFUND reach the new state-of-the-art results.
