论文标题
局部基于嵌套的嵌套命名实体识别为基于查询的序列标签
Local Hypergraph-based Nested Named Entity Recognition as Query-based Sequence Labeling
论文作者
论文摘要
人们对许多领域中嵌套命名实体的认识的学术兴趣越来越大。我们使用一种新型的本地超图方法来解决任务：我们首先提出了及其周围环境的开始令牌候选者，并生成相应的查询，然后使用基于查询的序列标签模块为每个候选人形成本地超图。最终令牌估计器用于校正超图并获得最终预测。与基于跨度的方法相比，我们的方法没有跨度抽样的高计算成本和失去长实体的风险。顺序预测使得在嵌套结构内的单词顺序中利用信息更容易，并且使用局部超图构建了更丰富的表示。实验表明，我们所提出的方法在所有四个嵌套数据集上的边距大量优于以前的所有基于超图和序列标记方法。它在ACE 2004数据集中获得了新的最先进的F1分数，并在其他三个Nested NER数据集上使用先前最先进的方法（ACE 2005，GENIA和KBP 2017）进行了竞争性F1分数。
There has been a growing academic interest in the recognition of nested named entities in many domains. We tackle the task with a novel local hypergraph-based method: We first propose start token candidates and generate corresponding queries with their surrounding context, then use a query-based sequence labeling module to form a local hypergraph for each candidate. An end token estimator is used to correct the hypergraphs and get the final predictions. Compared to span-based approaches, our method is free of the high computation cost of span sampling and the risk of losing long entities. Sequential prediction makes it easier to leverage information in word order inside nested structures, and richer representations are built with a local hypergraph. Experiments show that our proposed method outperforms all the previous hypergraph-based and sequence labeling approaches with large margins on all four nested datasets. It achieves a new state-of-the-art F1 score on the ACE 2004 dataset and competitive F1 scores with previous state-of-the-art methods on three other nested NER datasets: ACE 2005, GENIA, and KBP 2017.
