Neural Event Extraction from Scientific Literature

从科学文献中提取神经事件

• 批准号: 2324763
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2324763
• 关键词: Neural; Event; Extraction; Scientific; Literature

The main objective of the thesis is to investigate event extraction models able to extract multiple structures from text. Although many neural models have been proposed for event extraction, most have focused on flat events and flat entities. State-of-the art approaches to event extraction are pipeline systems, which independently addresses small tasks in sequence, to solve a more complex task. However, sequential processing may result in suboptimal overall performance when compared to approaching the subtasks in a joint manner. This can be attributed to the fact that errors resulting from any of a pipeline's early subtasks are propagated to the rest of the subtasks in the series. Also, pipeline-based approaches fail to capture inter-dependencies amongst the different subtasks. As a result, elements that are separately dealt with in a pipeline (e.g., named entities and events) cannot be used for mutual disambiguation. Only a handful of event extraction approaches have employed embeddings, and using only lexical and syntactic features of words.We will investigate novel neural network structured prediction models for event extraction, building on top of the contextual embeddings and simultaneously detecting entity/triggers, role layers, trigger-argument pairs and event layers. Our aim is to maximise both precision and high-coverage. By decomposing deep information extraction into separate components, we can consider each one as an individual system to support easy connectivity between components. The models of these components will support joint learning, thus the individual models have to share representations. To achieve high precision we will deploy joint training between different components of the event extraction pipeline and we will incorporate multi-task learning with other tasks, e.g. POS tagging, language models. We will also deal with complex data representations such as nested and disjoint entities, triggers and events and take into account semantic relational structure between words to improve performance. To achieve high coverage, we will investigate joint representation of linguistic units such as characters, sub-words, phrases and multi-word expressions which will enable more flexible and joint learning from external manually curated resources such as knowledge bases, ontologies, lexica and databases.

本文的主要目的是研究能够从文本中抽取出多种结构的事件抽取模型。尽管已经提出了许多用于事件提取的神经模型，但大多数都集中在平面事件和平面实体上。最先进的事件提取方法是流水线系统，它独立地按顺序处理小任务，以解决更复杂的任务。然而，顺序处理可能会导致次优的整体性能相比，以联合的方式接近的子任务。这可以归因于这样一个事实，即由管道的任何早期子任务产生的错误会传播到系列中的其余子任务。此外，基于流水线的方法无法捕获不同子任务之间的相互依赖性。因此，在流水线中单独处理的元素（例如，命名的实体和事件）不能用于相互消除歧义。只有少数的事件提取方法采用嵌入，只使用词汇和句法特征的words.We将研究新的神经网络结构化预测模型的事件提取，建立在上下文嵌入的顶部，同时检测实体/触发器，角色层，参数对和事件层。我们的目标是最大限度地提高精度和高覆盖率。通过将深度信息提取分解为单独的组件，我们可以将每个组件视为一个单独的系统，以支持组件之间的轻松连接。这些组件的模型将支持联合学习，因此各个模型必须共享表示。为了实现高精度，我们将在事件提取管道的不同组件之间部署联合训练，并将多任务学习与其他任务结合起来，例如POS标记，语言模型。我们还将处理复杂的数据表示，如嵌套和不相交的实体，触发器和事件，并考虑单词之间的语义关系结构，以提高性能。为了实现高覆盖率，我们将研究语言单位的联合表示，如字符，子词，短语和多词表达，这将使外部手动策划的资源，如知识库，本体，词汇和数据库，更灵活和联合学习。