知识驱动的事件双曲嵌入时序关系抽取方法研究

Exploration of Knowledge Driven Event Hyperbolic Embedding Temporal Relation Extraction Method

针对事件时间关系不对称的问题,采用将事件表示映射到双曲空间的方法,进行事件时序关系抽取。通过简单的运算,用预训练的词向量与外部知识构建事件的词嵌入表示。在公开发布的数据集上的实验结果表明,模型的F1值比基线模型普遍高2%,能够提升事件时序关系抽取的效果。

Aiming at the problem of asymmetric temporal relations of events,the event representation is mapped to hyperbolic space to extract temporal relations of events.The word embedding representation of the event is constructed by using the pre-trained word vector and external knowledge through simple operation.Experimental results on publicly released datasets show that the F1 value of the model is generally 2% higher than that of the baseline model,which can improve the effect of event temporal relation extraction.