Papers by Michaela Geierhos
Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) models are susceptible to gender bias . benchmark datasets are curated specifically for a given NLP task . |
| Approach: | They propose to filter out benchmark templates with a higher probability of detecting gender bias in NER models. |
| Outcome: | The proposed method is based on masked token prediction and tested in English and german using the corresponding fine-tuned BERT base model. |
ITER: Iterative Transformer-based Entity Recognition and Relation Extraction (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP generate structured information in an autoregressive manner, causing low throughput . authors propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Approach: | They propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and is faster than existing models. |
Random Splitting Negatively Impacts NER Evaluation: Quantifying and Eliminating the Overestimation of NER Performance (2025.findings-acl)
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| Challenge: | Named entity recognition models are evaluated on their ability to identify entity mentions in text. |
| Approach: | They propose a method to reduce the amount of entity contamination in NER datasets by a minimum cut algorithm. |
| Outcome: | The proposed method minimizes train-test entity leakage while ensuring near zero entity contamination. |