论文标题
PESE：使用基于指针网络的Encoder-Decoder体系结构提取事件结构
PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture
论文作者
论文摘要
事件提取的任务（EE）旨在从文本中找到事件和事件相关的参数信息，并以结构化格式表示它们。大多数以前的工作都试图通过分别识别多个子结构并汇总它们以获取完整的事件结构来解决问题。方法的问题在于，它无法识别事件参与者（事件触发者，参数和角色）之间的所有相互依存关系。在本文中，我们以唯一的元组格式表示每个事件记录，其中包含触发短语，触发类型，参数短语和相应的角色信息。我们提出的基于指针网络的编码器模型模型通过利用事件参与者之间的交互并为EE任务提供真正的端到端解决方案，从而在每个时间步骤中生成事件元组。我们在ACE2005数据集上评估了我们的模型，实验结果与最新方法相比，通过实现竞争性能来证明我们的模型有效性。
The task of event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format. Most previous works try to solve the problem by separately identifying multiple substructures and aggregating them to get the complete event structure. The problem with the methods is that it fails to identify all the interdependencies among the event participants (event-triggers, arguments, and roles). In this paper, we represent each event record in a unique tuple format that contains trigger phrase, trigger type, argument phrase, and corresponding role information. Our proposed pointer network-based encoder-decoder model generates an event tuple in each time step by exploiting the interactions among event participants and presenting a truly end-to-end solution to the EE task. We evaluate our model on the ACE2005 dataset, and experimental results demonstrate the effectiveness of our model by achieving competitive performance compared to the state-of-the-art methods.
