A fast and sound tagging method for discontinuous named-entity recognition
<|reference_start|>A fast and sound tagging method for discontinuous named-entity recognition: 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.<|reference_end|>
@article{corro2024a,
    title={A fast and sound tagging method for discontinuous named-entity
  recognition},
    author={Caio Corro},
    journal={arXiv preprint arXiv:2409.16243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16243},
    primaryClass={cs.CL}
}