论文标题
命名为实体识别为依赖性解析
Named Entity Recognition as Dependency Parsing
论文作者
论文摘要
命名实体识别（NER）是自然语言处理中的一项基本任务，与识别表达对实体的参考的文本跨度有关。 NER研究通常只专注于平坦的实体（扁平ner），而忽略了可以嵌套实体参考的事实，就像[[中国银行]]一样（Finkel and Manning，2009年）。在本文中，我们使用基于图的依赖性解析的想法，通过Biaffine模型为我们的模型提供对输入的全球视图（Dozat and Manning，2017年）。 Biaffine模型在我们用来探索所有跨度的句子中得分成对的开始和结束令牌，以便该模型能够准确预测命名实体。我们表明，该模型通过评估8个Corpora并在所有这些方面实现SOTA性能，对嵌套和扁平的NER都很好，精度获得了2.2个百分点。
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.
