Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition
Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition
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发表时间：
2016-12
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通讯作者：
Nut Limsopatham;Nigel Collier
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作者：
Nut Limsopatham;Nigel Collier
命名实体识别（NER）的端到端神经网络模型已经显示出在一般领域数据集（例如新闻专线）上实现有效的性能，而不需要额外的手工制作特征。然而，在生物医学领域，最近的研究表明，由于生物医学术语的复杂性（例如使用缩写词和复杂的基因名称），应该使用手工工程特征（例如正字法特征）来获得有效的性能。在这项工作中，我们提出了一种新的方法，允许基于长短期记忆（LSTM）的神经网络模型自动学习正字法特征并将其纳入生物医学NER模型。重要的是，我们的双向LSTM模型在端到端基础上学习和利用正字法特征。我们通过使用三个完善的生物医学数据集与现有的NER神经网络模型进行比较来评估我们的方法。我们的实验结果表明，所提出的方法在所有三个数据集上始终优于这些强基线。
End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in biomedical domain, recent studies have shown that hand-engineered features (e.g. orthographic features) should be used to attain effective performance, due to the complexity of biomedical terminology (e.g. the use of acronyms and complex gene names). In this work, we propose a novel approach that allows a neural network model based on a long short-term memory (LSTM) to automatically learn orthographic features and incorporate them into a model for biomedical NER. Importantly, our bi-directional LSTM model learns and leverages orthographic features on an end-to-end basis. We evaluate our approach by comparing against existing neural network models for NER using three well-established biomedical datasets. Our experimental results show that the proposed approach consistently outperforms these strong baselines across all of the three datasets.