---
title: "MetaMiners at SMM4H-HeaRD 2026: A Semantic-Structural"
description: "The paper addresses SMM4H-HeaRD 2026 Task 5. 12 references. Among them: 0."
authors: ["Claudia-Alexandra Ursu", "Alecsandru-Florin Soare"]
year: 2026
venue: "Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks"
source: "https://aclanthology.org/2026.smm4h-1.45/"
doi: "10.18653/v1/2026.smm4h-1.45"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ursu-2026-metaminers-smm4h-heard-2026-semantic/"
---
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# MetaMiners at SMM4H-HeaRD 2026: A Semantic-Structural Knowledge-Enriched Ensemble for SARS-CoV-2 Metadata Identification

**Authors:** Claudia-Alexandra Ursu and Alecsandru-Florin Soare · **Year:** 2026 · **Venue:** [Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks](https://md.research.elamur.ai/free/en/venues/smm4h/) ([Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2026](https://md.research.elamur.ai/free/en/venues/smm4h/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.smm4h-1.45/) · [PDF](https://aclanthology.org/2026.smm4h-1.45.pdf) · [DOI 10.18653/v1/2026.smm4h-1.45](https://doi.org/10.18653/v1/2026.smm4h-1.45) · [OpenAlex](https://openalex.org/W7166802079)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper addresses SMM4H-HeaRD 2026 Task 5. The task is a binary classification of sentences from PubMed articles about SARS-CoV-2 sequences. The goal is to decide whether a sentence reports patient metadata, such as age, sex, location or symptoms, tied to the sequenced samples. Sentences that mention such data in unrelated, technical or prior-study contexts must be rejected. This is useful because repositories like GISAID and GenBank often have empty metadata fields, and manual curation cannot keep up during a pandemic. The corpus has 22,147 sentences, and only 13.3% are positive.

The authors enrich the input text with regex-based tags marking:
- metadata values and their counts
- "anti-metadata" technical cues
- reporting versus procedural verbs
- sentence focus and negation
- scispaCy dependency frames that link attributes to a patient

On this enriched text they fine-tune four biomedical transformers, each with a different training trick. They then combine the models through a weighted average of logits. The ensemble reached F1 0.786 on the test set and ranked first, ahead of the earlier 0.776 BiomedBERT-Large baseline.

## Key points
- **Data split:** 15,504 train / 2,214 validation / 4,429 test sentences, with a strong class imbalance (13.3% positive).
- **Ensemble members and validation F1:**
  - BioLinkBERT-Large with Focal Loss: 0.822
  - BiomedBERT-base-abstract with semantic frames and weighted cross-entropy: 0.815
  - PubMedBERT-fulltext with the bottom 8 layers frozen: 0.819
  - BioLinkBERT-Large with FGM adversarial training and label smoothing: 0.800
- **Ensemble result:** weights of about 0.18–0.27 per model and a threshold of 0.55 gave validation F1 0.851 (P 0.836, R 0.867) and test F1 0.786, placing 1st.
- **Negation handling:** the system scans a 60-character window after a negation cue and adds a [NEG] prefix to metadata found there. These negated items are excluded from the [META:N] count to cut false positives.
- **Error analysis:** aggregate density tags ([META]/[ANTI]) were a main source of false positives. The authors plan to replace them with type-specific markers. No formal ablation study was run.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Bucharest](https://md.research.elamur.ai/free/en/universities/university-of-bucharest/) — University, Bucharest, Romania · 21 works · 516 citations

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## Cite

Cite as: Claudia-Alexandra Ursu and Alecsandru-Florin Soare. 2026. MetaMiners at SMM4H-HeaRD 2026: A Semantic-Structural Knowledge-Enriched Ensemble for SARS-CoV-2 Metadata Identification. Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ursu2026metaminers,
  title = {{MetaMiners at SMM4H-HeaRD 2026: A Semantic-Structural Knowledge-Enriched Ensemble for SARS-CoV-2 Metadata Identification}},
  author = {Claudia-Alexandra Ursu and Alecsandru-Florin Soare},
  booktitle = {Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks},
  year = {2026},
  doi = {10.18653/v1/2026.smm4h-1.45},
  url = {https://doi.org/10.18653/v1/2026.smm4h-1.45}
}
```

- [← previous MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization](https://md.research.elamur.ai/free/en/papers/xin-2026-metamem-evolving-meta-memory-knowledge/)
- [next → Metaphor Identification in Spanish Oncological Discourse: The Role of Explicit Meaning in Low-Resource Settings](https://md.research.elamur.ai/free/en/papers/pitarch-2026-metaphor-identification-spanish-oncological-discourse/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/41/)

## References

12 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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2. Franck Dernoncourt and Ji Young Lee. 2017. [Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts](https://md.research.elamur.ai/free/en/papers/dernoncourt-2017-pubmed-200k-rct-dataset-sequential/). In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 308–313, Taipei, Taiwan. Asian Federation of N…
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7. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Huang, Kevin Gimpel, Wei-Chen Chen, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021. [Domain-specific language model pretraining for biomedical natural language processing](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/). ACM Transactions on Computing for Healthcare, 3(1):1…
8. Ari Z. Klein, Davy Weissenbacher, Karen O’Connor, Amir Elyaderani, Ivan Flores Amaro, Takeshi Onishi, Su Golder, Kaelen Spiegel, Matthew Scotch, and Graciela Gonzalez-Hernandez. 2025. Detection of patient metadata in published articles for genomic epidemiology using machine learning and large langua… [find](https://scholar.google.com/scholar?q=Ari+Z%2E+Klein%2C+Davy+Weissenbacher%2C+Karen+O%E2%80%99Connor%2C+Amir+Elyaderani%2C+Ivan+Flores+Amaro%2C+Takeshi+Onishi%2C+Su+Golder%2C+Kaelen+Spiegel%2C+Matthew+Scotch%2C+and+Graciela+Gonzalez%2DHernandez%2E+2025%2E+Detection+of+pat)
9. Guillermo Lopez-Garcia, Jose Miguel Acitores Cortina, Jacob Berkowitz, Joey Chan, Ganesh Chandrasekar, Sumon Kanti Dey, Ivan Flores Amaro, Fernando Gallego, Lauren Gryboski, Ari Z Klein, Martin Krallinger, Salvador Lima-López, Tomohiro Nishiyama, Lisa Raithel, Ahmad Rezaie Mianroodi, Roland Roller,… [find](https://scholar.google.com/scholar?q=Guillermo+Lopez%2DGarcia%2C+Jose+Miguel+Acitores+Cortina%2C+Jacob+Berkowitz%2C+Joey+Chan%2C+Ganesh+Chandrasekar%2C+Sumon+Kanti+Dey%2C+Ivan+Flores+Amaro%2C+Fernando+Gallego%2C+Lauren+Gryboski%2C+Ari+Z+Klein%2C+Martin+Kralli)
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*Updated 1 November 2026*