Nested Named Entity Recognition as Single-Pass Sequence Labeling

This repository contains the official implementation for the paper: Nested Named Entity Recognition as Single-Pass Sequence Labeling Alberto Muñoz-Ortiz, David Vilares, Caio Corro, and Carlos Gómez-Rodríguez Presented at Findings of EMNLP 2025 in Suzhou, China.

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Overview

This project provides tools to linearize nested entity structures into sequence labeling formats and train models using various encoders (like BERT, RoBERTa, etc.). It supports multiple encoding strategies including REL, ABS, JUX, DYN, and 4EC.

Project Structure

The repository is organized as follows:

• src/: Core logic, including data utilities and MaChAmp configurations.
• scripts/: Entry-point scripts for training, evaluation, and data analysis.
• parameter_configs/: Configuration templates for model training.
• data/: Directory for storing datasets (should contain train.data, dev.data, and test.data).
• logs/: Directory where training outputs and results are stored.

Installation

1. Clone the repository with submodules:

   git clone --recurse-submodules https://github.com/amunozo/nner_as_sl.git
   cd nner_as_sl

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

Training

To train the NNER models:

python scripts/train.py \
  --dataset genia \
  --encoder bert-base-uncased \
  --encoding REL \
  --n_seeds 1

Evaluation

To evaluate the trained models:

python scripts/evaluate.py \
  --dataset genia \
  --encoder bert-base-uncased \
  --encoding REL \
  --device 0

Data Format

Input data should have a line with the tokens followed by a line with the annotated entities. Entities are defined by a triple start, end, type separated by |.

Example:

IL-2 gene expression and NF-kappa B activation through CD28 requires reactive oxygen production by 5-lipoxygenase .
0,1 G#DNA|4,5 G#protein|8,8 G#protein|14,14 G#protein

Citation

If you use this code or our findings in your research, please cite:

@inproceedings{munoz-ortiz-etal-2025-nested,
    title = "Nested Named Entity Recognition as Single-Pass Sequence Labeling",
    author = "Mu{\~n}oz-Ortiz, Alberto and
      Vilares, David and
      Corro, Caio and
      G{\\'o}mez-Rodr{\\'i}guez, Carlos",
    editor = "Christodoulopoulos, Christos and
      Chakraborty, Tanmoy and
      Rose, Carolyn and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.530/",
    doi = "10.18653/v1/2025.findings-emnlp.530/",
    pages = "9993--10002",
}

Contact

For any questions or issues, please contact the main author: Alberto Muñoz-Ortiz - alberto.munoz.ortiz@udc.es

Acknowledgments

This work was funded by SCANNER-UDC (PID2020-113230RB-C21) funded by MICIU/AEI/10.13039/501100011033; Xunta de Galicia (ED431C 2024/02); GAP (PID2022-139308OA-I00) funded by MICIU/AEI/10.13039/501100011033/ and by ERDF, EU; Grant PRE2021-097001 funded by MICIU/AEI/10.13039/501100011033 and by ESF+ (predoctoral training grant associated to project PID2020-113230RB-C21); LATCHING (PID2023-147129OB-C21) funded by MICIU/AEI/10.13039/501100011033 and ERDF; TSI-100925-2023-1 funded by Ministry for Digital Transformation and Civil Service and “NextGenerationEU” PRTR; and Centro de Investigación de Galicia ‘‘CITIC’’, funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUSA).

This research project was made possible through the access granted by the Galician Supercomputing Center (CESGA) to its supercomputing infrastructure. The supercomputer FinisTerrae III and its permanent data storage system have been funded by the NextGeneration EU 2021 Recovery, Transformation and Resilience Plan, ICT2021-006904, and also from the Pluriregional Operational Programme of Spain 2014-2020 of the European Regional Development Fund (ERDF), ICTS-2019-02-CESGA-3, and from the State Programme for the Promotion of Scientific and Technical Research of Excellence of the State Plan for Scientific and Technical Research and Innovation 2013-2016 State subprogramme for scientific and technical infrastructures and equipment of ERDF, CESG15-DE-3114.

Caio Corro has received funding from the French Agence Nationale pour la Recherche under grant agreement InExtenso ANR-23-IAS1-0004 and SEMIAMOR ANR-23-CE23-0005.