A fast and sound tagging method for discontinuous named-entity recognition

• URL: http://arxiv.org/abs/2409.16243v1
• Date: Tue, 24 Sep 2024 17:07:45 GMT
• Title: A fast and sound tagging method for discontinuous named-entity recognition
• Abstract summary: We introduce a novel tagging scheme for discontinuous named entity recognition. We rely on a weighted finite state automaton for both marginal and maximum a posteriori inference. We evaluate our approach on three English datasets in the biomedical domain.
• Score: 8.48487186427764
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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