不连续NER的一种有效的转移模型
An Effective Transition-based Model for Discontinuous NER
摘要：
与通用领域中广泛使用的命名实体识别（NER）数据集不同，生物医学NER数据集通常包含由不连续跨度组成的提及。传统的序列标记技术编码马尔可夫假设是有效的，但排除恢复这些提及。我们提出了一个简单，有效的过渡为基础的模型与通用神经编码的不连续NER。通过在三个生物医学数据集上的大量实验，我们证明了我们的模型可以有效地识别不连续提及，而不会牺牲连续提及的准确性。
英文摘要：
Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions. We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER. Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.
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