Hypergraph based Understanding for Document Semantic Entity Recognition
This addresses the problem of extracting detailed semantic information from documents for applications like document understanding, though it is incremental as it builds on GraphLayoutLM.
The paper tackles semantic entity recognition in visually-rich documents by proposing a hypergraph attention framework (HGA) that simultaneously focuses on entity boundaries and categories, improving performance over existing models. Results show state-of-the-art performance on datasets like FUNSD and XFUND.
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.