# Learning to Extract Structured Entities Using Language Models

arXiv: https://arxiv.org/abs/2402.04437 · HTML: https://mlsanity.com/paper/2402.04437/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [A Survey of Deep Learning Methods for Relation Extraction](https://mlsanity.com/paper/1705.03645/) (2017)
- [DetIE: Multilingual Open Information Extraction Inspired by Object Detection](https://mlsanity.com/paper/2206.12514/) (2022)
- [A Neural Entity Coreference Resolution Review](https://mlsanity.com/paper/1910.09329/) (2019)

## Built on by (most cited first, 8 of 8)

- [LLM-Aided Customizable Profiling of Code Data Based On Programming Language Concepts](https://mlsanity.com/paper/2503.15571/) (2025)
- [An archaeological Catalog Collection Method Based on Large Vision-Language Models](https://mlsanity.com/paper/2412.20088/) (2024)
- [From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction](https://mlsanity.com/paper/2604.27906/) (2026)
- [Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation](https://mlsanity.com/paper/2605.16748/) (2026)
- [Extracting Research Instruments from Educational Literature Using LLMs](https://mlsanity.com/paper/2505.21855/) (2025)
- [Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model](https://mlsanity.com/paper/2509.08381/) (2025)
- [Enriching Historical Records: An OCR and AI-Driven Approach for Database Integration](https://mlsanity.com/paper/2512.23710/) (2025)
- [Adapt Semantics, Not Structure: Few-Instance Schema Calibration for Scientific PDF Extraction](https://mlsanity.com/paper/2609.34841/) (2026)

## Abstract (arXiv)

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text. Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation. We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP (AESOP) metric, designed to appropriately assess model performance. Later, we introduce a new Multistage Structured Entity Extraction (MuSEE) model that harnesses the power of LMs for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages. Quantitative and human side-by-side evaluations confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction. Our source code and datasets are available at https://github.com/microsoft/Structured-Entity-Extraction.