Biomedical Event Extraction based on Knowledge-driven Tree-LSTM
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM
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DOI：
10.18653/v1/n19-1145
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发表时间：
2019
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影响因子：
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通讯作者：
Diya Li;Lifu Huang;Heng Ji;Jiawei Han
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文献类型：
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作者：
Diya Li;Lifu Huang;Heng Ji;Jiawei Han
生物医学领域的事件提取比一般新闻领域的事件提取更具挑战性，因为它需要更广泛地获取特定领域的知识和更深入地理解复杂的上下文。为了更好地编码上下文信息和外部背景知识，我们提出了一种新的知识库（KB）驱动的树结构长短期记忆网络（Tree-LSTM）框架，包含两种新类型的特征：（1）依赖结构，以捕获广泛的上下文;（2）通过实体链接来自外部本体的实体属性（类型和类别描述）。我们在BioNLP共享任务上使用Genia数据集评估了我们的方法，并获得了新的最先进的结果。此外，定量和定性研究都证明了Tree-LSTM的进步和生物医学事件提取的外部知识表示。
Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To better encode contextual information and external background knowledge, we propose a novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework, incorporating two new types of features: (1) dependency structures to capture wide contexts; (2) entity properties (types and category descriptions) from external ontologies via entity linking. We evaluate our approach on the BioNLP shared task with Genia dataset and achieve a new state-of-the-art result. In addition, both quantitative and qualitative studies demonstrate the advancement of the Tree-LSTM and the external knowledge representation for biomedical event extraction.