Biomedical event extraction is an important branch of biomedical information extraction. Trigger extraction is the most essential sub-task in event extraction, which has been widely concerned. Existing trigger extraction studies are mostly based on conventional machine learning or neural networks. But they neglect the ambiguity of word representations and the insufficient feature extraction by shallow hidden layers. In this paper, trigger extraction is treated as a sequence labeling problem. We introduce the language model to dynamically compute contextualized word representations and propose a multi-layer residual bidirectional long short-term memory (BiLSTM) architecture. First, we concatenate contextualized word embedding, pretrained word embedding and character-level embedding as the feature representations, which effectively solves the tokens’ ambiguity in biomedical corpora. Then, the designed BiLSTM block with residual connection and gated multi-layer perceptron is adopted to extract features iteratively. This architecture improves the ability of our model to capture information and avoids gradient exploding or vanishing. Finally, we combine the multi-layer residual BiLSTM with CRF layer to obtain more reasonable label sequences. Comparing with other state-of-the-art methods, the proposed model achieves the competitive performance (F1-score: 80.74%) on the biomedical multi-level event extraction (MLEE) corpus without any manual participation and feature engineering.

生物医学事件提取是生物医学信息提取的重要分支。触发提取是事件提取中最重要的子任务，已引起广泛关注。现有的触发器提取研究主要基于常规的机器学习或神经网络。但是他们忽略了单词表示的歧义性以及浅层隐藏层对特征提取的不足。在本文中，触发器提取被视为序列标记问题。我们介绍了语言模型以动态计算上下文化的词表示形式，并提出了多层残差双向长短期记忆（BiLSTM）体系结构。首先，我们将上下文化词嵌入，预训练词嵌入和字符级嵌入作为特征表示，有效解决了令牌在生物医学语料库中的歧义。然后，采用设计的带有残余连接和门控多层感知器的BiLSTM块来迭代提取特征。这种架构提高了我们的模型捕获信息的能力，并避免了梯度爆炸或消失。最后，我们将多层残留BiLSTM与CRF层结合起来以获得更合理的标记序列。与其他最新方法相比，该模型在生物医学多级事件提取（MLEE）语料库上实现了竞争性能（F1-得分：80.74％），而无需任何人工参与和特征工程。这种架构提高了我们的模型捕获信息的能力，并避免了梯度爆炸或消失。最后，我们将多层残留BiLSTM与CRF层结合起来以获得更合理的标记序列。与其他最新方法相比，该模型在生物医学多级事件提取（MLEE）语料库上实现了竞争性能（F1-得分：80.74％），而无需任何人工参与和特征工程。这种架构提高了我们的模型捕获信息的能力，并避免了梯度爆炸或消失。最后，我们将多层残留BiLSTM与CRF层结合起来以获得更合理的标记序列。与其他最新方法相比，该模型在生物医学多级事件提取（MLEE）语料库上实现了竞争性能（F1-得分：80.74％），而无需任何人工参与和特征工程。