论文标题
重置：通过学习检索和选择从科学文本和表中提取N-美联社的关系
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
论文作者
论文摘要
我们研究了从科学文章中提取N- ARY关系元素的问题。此任务具有挑战性，因为目标知识元组可以驻留在文档的多个部分和模式中。我们提出的方法将此任务分解为两个阶段的过程，该过程首先检索最相关的段落/表，然后从检索到的组件中选择目标实体。对于高级检索阶段，Resel设计了一个简单有效的功能集，该集合捕获了查询和组件之间的多级词汇和语义相似性。对于低级选择阶段，RESEL设计了一个跨模式实体相关图以及多视图架构，该体系结构对实体之间的语义和文档结构关系进行建模。我们对三个科学信息提取数据集进行的实验表明，重新估计优于最先进的基线。
We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method ReSel decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component. For the high-level retrieval stage, ReSel designs a simple and effective feature set, which captures multi-level lexical and semantic similarities between the query and components. For the low-level selection stage, ReSel designs a cross-modal entity correlation graph along with a multi-view architecture, which models both semantic and document-structural relations between entities. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
