Papers by Aron Henriksson
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data (2022.lrec-1)
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| Challenge: | Automatic de-identification systems introduce errors due to their imperfect precision and may negatively impact the utility of the de-identified dataset. |
| Approach: | They propose to de-identifie a large clinical corpus in Swedish by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. |
| Outcome: | The proposed models are safe to distribute to other academic researchers and reduce privacy risks. |
Data-Constrained Synthesis of Training Data for De-Identification (2025.acl-long)
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| Challenge: | sensitive domains lack widely available datasets due to privacy risks . recent studies have focused on evaluating the privacy of the synthetic text . |
| Approach: | They domain-adapt LLMs to clinical domain and generate synthetic clinical texts . they then generate NER models that can be annotated with tags for PII . |
| Outcome: | The proposed model performs better than the original model using smaller datasets. |
FoodSafeSum: Enabling Natural Language Processing Applications for Food Safety Document Summarization and Analysis (2025.findings-emnlp)
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Juli Bakagianni, Korbinian Randl, Guido Rocchietti, Cosimo Rulli, Franco Maria Nardini, Salvatore Trani, Aron Henriksson, Anna Romanova, John Pavlopoulos
| Challenge: | a lack of structured datasets hinders natural language processing research . a new dataset of food safety documents and related metadata is presented . |
| Approach: | They present a dataset of human-written and Large Language Model (LLM)-generated food safety documents . they evaluate their utility on three NLP tasks directly reflecting food safety practices . |
| Outcome: | The proposed dataset performs comparably or better than human summaries on three NLP tasks . it also shows clustering of summary for event tracking and compliance monitoring . |
Evaluating Pretraining Strategies for Clinical BERT Models (2022.lrec-1)
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| Challenge: | Existing generic language models in specialized domains may be sub-optimal due to domain differences. |
| Approach: | They propose various strategies for adapting a generic language model to the target domain and various forms of vocabulary modifications to fine-tune it. |
| Outcome: | The proposed strategies outperform a general-domain language model but little difference in performance between the models. |
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification (2024.findings-acl)
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| Challenge: | Contaminated or adulterated food poses a substantial risk to human health. |
| Approach: | They present a dataset of 7,546 text messages describing public food recalls. |
| Outcome: | The proposed model outperforms RoBERTa and XLM-R on classes with low support while reducing energy consumption. |