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

arXiv:2609.00832 · cs.CL · Submitted 2026-09-01 · Read on arXiv

cs.CL

Submitted: 2026-09-01

Updated: 2026-09-01

Comments: Accepted at CLEF 2026: the 17th Conference and Labs of the Evaluation Forum

Code: https://github.com/MMartinelli-hub/GBIE26_TWIX

License: http://creativecommons.org/licenses/by/4.0/

The gist: The exponential growth of scientific publications calls for automatic Information Extraction (IE) systems to support knowledge discovery.

Terminology

Transformer — 41× in this paper · explained in 2 episodes
Relation Extraction — 19× in this paper
Named Entity Recognition — 14× in this paper
Fine-Tuning — 13× in this paper · explained in 5 episodes
Association for Computational Linguistics — 12× in this paper
Information Extraction — 6× in this paper
Named Entity Recognition and Disambiguation — 4× in this paper
Transformer Models — 4× in this paper · explained in 2 episodes
Two-stage training — 4× in this paper · explained in 2 episodes
Benchmark — 3× in this paper · explained in 2 episodes
Named Entity Linking — 3× in this paper
Term Extraction — 3× in this paper
AdamW Optimizer — 2× in this paper · explained in 1 episode
Embeddings — 2× in this paper · explained in 2 episodes
Named Entity Recognition (NER) — 2× in this paper · explained in 2 episodes
Ontology — 2× in this paper · explained in 2 episodes
Unified Framework — 2× in this paper · explained in 5 episodes
Contrastive Learning — 1× in this paper · explained in 5 episodes
Extract First Classify Later — 1× in this paper
F1 Score — 1× in this paper · explained in 3 episodes
Generalization — 1× in this paper · explained in 13 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Large Language Model — 1× in this paper
Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
Regularization — 1× in this paper · explained in 3 episodes
Term Classification — 1× in this paper
Two-Stage Framework — 1× in this paper · explained in 2 episodes

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.

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

Terminology

Abstract

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