mlsanityData to 14 Oct 2026

GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction

arXiv 2404.12491 ↗First posted 18 Apr 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text. In this paper, we propose a novel approach to this task by formulating it as graph structure learning (GSL). By formulating IE as GSL, we enhance the model's ability to dynamically refine and optimize the graph structure during the extraction process. This formulation allows for better interaction and structure-informed decisions for entity and relation prediction, in contrast to previous models that have separate or untied predictions for these tasks. When compared against state-of-the-art baselines on joint entity and relation extraction benchmarks, our model, GraphER, achieves competitive results.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 71 references that are in the map. First posted on arXiv 18 Apr 2024.

Builds on

39 of 71 references.

References · 39

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27 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Graph RAG-Tool Fusioncited by 15
  2. 2026GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extractioncited by 1