结合事件知识利用语义空间增强事件检测

Jinshang Luo, Mengshu Hou
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引用次数: 0

摘要

事件检测(ED)的目的是识别文本中的触发器并确定适当的类别。事件检测经常受到标记数据稀缺性的阻碍,并且大多数当前方法都忽略了事件之间的相关性。针对这些问题,提出了一种基于语义空间和事件知识的事件检测框架(EDSSEK)。为了引入事件知识,在精心构建的领域语料库的基础上对预训练模型进行扩展和微调。事件类型的表示通过预训练模型进行编码,并映射到语义空间。使用文档级注意机制获得事件触发器的特征向量,然后将其投影到相同的向量空间中。通过最小化事件触发器与相关类型之间的距离来训练文档嵌入网络。在基准数据集上的实验表明,EDSSEK方法优于其他最先进的方法,证明了语义空间与事件知识相结合的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting Semantic Space to Enhance Event Detection Combined with Event Knowledge
Event detection (ED) aims to entail the identification of triggers in text and the determination of the appropriate categories. Event detection is frequently hampered by labeled data scarcity, and most current methods ignore the correlations between events. Aiming at the issues, a novel event detection framework leveraging semantic space and event knowledge (EDSSEK) is proposed. To introduce event knowledge, the pre-trained model is extended and fine-tuned based on the elaborately constructed domain corpus. The presentations of event types are encoded through the pre-trained model and mapped into the semantic space. The feature vectors of event triggers are gained using a document-level attention mechanism and then projected into the same vector space. The document embedding networks are trained by minimizing the distances between the event triggers and the relevant types. Experiments on benchmark datasets demonstrate that EDSSEK outperforms other state-of-the-art methods, and justify the effectiveness of semantic space combined with event knowledge.
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