论文标题
开放信息提取的句法多视图学习
Syntactic Multi-view Learning for Open Information Extraction
论文作者
论文摘要
开放信息提取（OpenIE）旨在从开放域句子中提取关系元素。基于规则的传统或统计模型是根据句法解析器确定的句子句法结构开发的。但是，以前的神经开放式模型在探索后面有用的句法信息。在本文中，我们将选区和依赖树建模为单词级图，并使神经开放式从句法结构中学习。为了更好地融合这两个图的异质信息，我们采用多视图学习来捕获它们的多个关系。最后，鉴定的选区和依赖性表示形式与元组生成的句子语义表示汇总。实验表明，选区和依赖性信息以及多视图学习都是有效的。
Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models have been developed based on syntactic structures of sentences, identified by syntactic parsers. However, previous neural OpenIE models under-explore the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from both graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.
