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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.13870 |
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| _version_ | 1866918133356298240 |
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| author | Jalocha, Maciej Schmidt, Johan Hausted Michelseen, William |
| author_facet | Jalocha, Maciej Schmidt, Johan Hausted Michelseen, William |
| contents | The field of cybersecurity NER lacks standardized labels, making it challenging to combine datasets. We investigate label unification across four cybersecurity datasets to increase data resource usability. We perform a coarse-grained label unification and conduct pairwise cross-dataset evaluations using BiLSTM models. Qualitative analysis of predictions reveals errors, limitations, and dataset differences. To address unification limitations, we propose alternative architectures including a multihead model and a graph-based transfer model. Results show that models trained on unified datasets generalize poorly across datasets. The multihead model with weight sharing provides only marginal improvements over unified training, while our graph-based transfer model built on BERT-base-NER shows no significant performance gains compared BERT-base-NER. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13870 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Label Unification for Cross-Dataset Generalization in Cybersecurity NER Jalocha, Maciej Schmidt, Johan Hausted Michelseen, William Computation and Language The field of cybersecurity NER lacks standardized labels, making it challenging to combine datasets. We investigate label unification across four cybersecurity datasets to increase data resource usability. We perform a coarse-grained label unification and conduct pairwise cross-dataset evaluations using BiLSTM models. Qualitative analysis of predictions reveals errors, limitations, and dataset differences. To address unification limitations, we propose alternative architectures including a multihead model and a graph-based transfer model. Results show that models trained on unified datasets generalize poorly across datasets. The multihead model with weight sharing provides only marginal improvements over unified training, while our graph-based transfer model built on BERT-base-NER shows no significant performance gains compared BERT-base-NER. |
| title | Label Unification for Cross-Dataset Generalization in Cybersecurity NER |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.13870 |