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) wellformedness of predicted tag sequences is ensured via the automaton structure and (2) there is an unambiguous mapping between wellformed 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.