TWIX: a Two-Stage Approach for End-To-End Named Entity Recognition and Relation Extraction

2026-09-01

AI Summary

为解决科学出版物中信息提取问题，提出TWIX方法，通过两阶段框架提高命名实体识别和关系抽取的精度与召回率。

Abstract

The exponential growth of scientific publications calls for automatic Information Extraction (IE) systems to support knowledge discovery. In this context, the GutBrainIE benchmark evaluates Named Entity Recognition (NER), Named Entity Recognition and Disambiguation (NERD), and Relation Extraction (RE) systems in the gut-brain axis domain. We propose Two-stage Workflow for Information eXtraction (TWIX), an end-to-end IE pipeline featuring three interconnected modules, each leveraging a two-stage framework to solve all four GutBrainIE subtasks. Evaluation on the development and test sets shows that our method substantially outperforms the baseline by a wide margin, while also ranking first among all participant submissions across all subtasks. These results indicate that the proposed two-stage pipeline effectively improves both precision and recall in practical settings.

Problem

• Information Extraction
• Named Entity Recognition
• Relation Extraction
• Gut-Brain Axis

Innovation

• Two-stage Approach
• End-to-End
• Named Entity Recognition
• Relation Extraction
• GutBrainIE