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ETER: a new metric for the evaluation of hierarchical named entity recognition

机译：ETET：评估分层名为实体识别的新度量标准

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This paper addresses the question of hierarchical named entity evaluation. In particular, we focus on metrics to deal with complex named entity structures as those introduced within the Quaero project. The intended goal is to propose a smart way of evaluating partially correctly detected complex entities, beyond the scope of traditional metrics. None of the existing metrics are fully adequate to evaluate the proposed Quaero task involving entity detection, classification and decomposition. We are discussing the strong and weak points of the existing metrics. We then introduce a new metric, the Entity Tree Error Rate (ETER), to evaluate hierarchical and structured named entity detection, classification and decomposition. The ETER metric builds upon the commonly accepted SER metric, but it takes the complex entity structure into account by measuring errors not only at the slot (or complex entity) level but also at a basic (atomic) entity level. We are comparing our new metric to the standard one using first some examples and then a set of real data selected from the ETAPE evaluation results.
机译：本文讨论了分层命名实体评估的问题。特别是，我们专注于指标处理复杂的命名实体结构，因为Quaero项目中引入的那些。预期目标是提出一种智能方式来评估部分正确检测到的复杂实体，超出传统指标的范围。没有现有的指标完全足以评估涉及实体检测，分类和分解的提议的Quaero任务。我们正在讨论现有度量的强大和弱点。然后，我们介绍一个新的度量标准，实体树错误率（etet），以评估分层和结构命名实体检测，分类和分解。 eTet度量标准在常见的SER指标上构建，但通过测量不仅在插槽（或复杂实体）级别的错误，而且在基本（原子）实体级别的误差来考虑复杂的实体结构。我们正在使用从Etape评估结果中选择的一组真实数据来将新的公制与标准的标准进行比较。

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