Hypergraph based Understanding for Document Semantic Entity Recognition

TL;DR

This paper introduces HGA, a hypergraph attention framework for document semantic entity recognition that improves boundary and category detection, achieving state-of-the-art results on multiple datasets.

Contribution

The paper proposes a novel hypergraph attention model, HGALayoutLM, enhancing semantic entity recognition by jointly modeling entity boundaries and categories.

Findings

Achieves state-of-the-art results on FUNSD and XFUND datasets.

Effectively improves semantic entity recognition performance.

Demonstrates the benefit of hypergraph attention in document understanding.

Abstract

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.