Summary of Hypergraph Based Understanding For Document Semantic Entity Recognition, by Qiwei Li et al.
Hypergraph based Understanding for Document Semantic Entity Recognition
by Qiwei Li, Zuchao Li, Ping Wang, Haojun Ai, Hai Zhao
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel hypergraph attention document semantic entity recognition framework, HGA, is introduced to simultaneously focus on entity boundaries and categories in visually-rich documents. This framework uses hypergraph attention to analyze the position relationship between text nodes and the relation between text content, achieving better performance in semantic information analysis compared to existing models. By applying this method to GraphLayoutLM, a new semantic entity recognition model, HGALayoutLM, is constructed. Experimental results on FUNSD, CORD, XFUND, and SROIE demonstrate that HGA improves the performance of semantic entity recognition tasks based on the original model, with state-of-the-art results achieved on FUNSD and XFUND. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HGA is a new way to understand documents by identifying the types of text and finding where these texts start and stop. It’s like a superpower for computers that lets them read and understand written content better. The old way of doing this only focused on categories, but HGA does both boundaries and categories at the same time. This makes it much more powerful and accurate. We tested HGA with some famous datasets and it outperformed the old way. |
Keywords
» Artificial intelligence » Attention
