基于注意力机制的双向长短时记忆网络模型突发事件演化关系抽取

针对现有突发事件关系抽取研究多集中于因果关系抽取而忽略了其他演化关系的问题,为了提高应急决策中信息抽取的完备性,应用一种基于注意力机制的双向长短时记忆(LSTM)网络模型进行突发事件演化关系抽取。首先,结合突发事件演化关系的概念,构建演化关系模型并进行形式化定义,依据模型对突发事件语料进行标注;其次,搭建双向LSTM网络结构,并引入注意力机制计算注意力概率以突出关键词汇在文本中的重要程度;最终,使用搭建的网络模型进行演化关系抽取得到结果。在演化关系抽取实验中,相对于现有因果关系抽取方法,所提方法不仅抽取出更加充分的演化关系,为突发事件应急决策提供了更完善的信息;同时,在正确率、召回率和F1分数上分别平均提升了7.3%、6.7%和7.0%,有效提高了突发事件演化关系抽取的准确性。

Evolution relationship extraction of emergency based on attention-based bidirectional long short-term memory network model

Concerning the problem that existing study of emergency relationship extraction mostly focuses on causality extraction while neglects other evolutions, in order to improve the completeness of information extracted in emergency decision-making, a method based on attention-based bidirectional Long Short-Term Memory (LSTM) model was used to extract the evolution relationship. Firstly, combined with the concept of evolution relationship in emergencies, an evolution relationship model was constructed and given the formal definition, and the emergency corpus was labeled according to the model. Then, a bidirectional LSTM network was built and attention mechanism was introduced to calculate the attention probability to highlight the importance of the key words in the text. Finally, the built network model was used to extract the evolution relationship. In the evolution relationship extraction experiments, compared with the existing causality extraction methods, the proposed method can extract more sufficient evolution relationship for emergency decision-making. At the same time, the average precision, recall and F1_score are respectively increased by 7.3%, 6.7% and 7.0%, which effectively improves the accuracy of the evolution relationship extraction of emergency.