A fast and sound tagging method for discontinuous named-entity recognition
Abstract
We introduce a novel tagging scheme for discontinuous named entity recognition based on an explicit description of the inner structure of discontinuous mentions. We rely on a weighted finite state automaton for both marginal and maximum a posteriori inference. As such, our method is sound in the sense that (1) well-formedness of predicted tag sequences is ensured via the automaton structure and (2) there is an unambiguous mapping between well-formed sequences of tags and (discontinuous) mentions. We evaluate our approach on three English datasets in the biomedical domain, and report comparable results to state-of-the-art while having a way simpler and faster model.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2024
- DOI:
- 10.48550/arXiv.2409.16243
- arXiv:
- arXiv:2409.16243
- Bibcode:
- 2024arXiv240916243C
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- EMNLP 2024