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There has been a growing academic interest in the recognition of nested namedentities in many domains. We tackle the task with a novel localhypergraph-based method: We first propose start token candidates and generatecorresponding queries with their surrounding context, then use a query-basedsequence labeling module to form a local hypergraph for each candidate. An endtoken estimator is used to correct the hypergraphs and get the finalpredictions. Compared to span-based approaches, our method is free of the highcomputation cost of span sampling and the risk of losing long entities.Sequential prediction makes it easier to leverage information in word orderinside nested structures, and richer representations are built with a localhypergraph. Experiments show that our proposed method outperforms all theprevious hypergraph-based and sequence labeling approaches with large marginson all four nested datasets. It achieves a new state-of-the-art F1 score on theACE 2004 dataset and competitive F1 scores with previous state-of-the-artmethods on three other nested NER datasets: ACE 2005, GENIA, and KBP 2017.

Local Hypergraph-based Nested Named Entity Recognition as Query-based Sequence Labeling