论文标题
语义信息层次结构事件建模
Semantically-informed Hierarchical Event Modeling
论文作者
论文摘要
先前的工作表明，具有语义知识的耦合顺序的潜在变量模型可以改善事件建模方法的代表性能力。在这项工作中，我们提出了一个新颖的，双重的层次结构，半监督的事件建模框架，该框架提供了结构性层次结构，同时还考虑了本体论的层次结构。我们的方法由结构化潜在变量的多层组成，其中每个连续的层都会压缩并抽象以前的层。我们通过在事件类型上定义的结构化本体论知识的注入来指导这种压缩：重要的是，我们的模型允许部分注入语义知识，并且不取决于在语义本体的任何特定级别上观察实例。在两个不同的数据集和四个不同的评估指标中，我们证明了我们的方法能够以最高为8.5％的先前最新方法，这证明了结构化和语义层次结构知识对事件建模的好处。
Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consists of multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches by up to 8.5%, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.
