DOCoR: Document-level OpenIE with Coreference Resolution

S. Yong, Kuicai Dong, Aixin Sun
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Abstract

Open Information Extraction (OpenIE) extracts relational fact tuples in the form of from text. Most existing OpenIE solutions operate at sentence level and extract relational tuples solely from a sentence. However, many sentences exist as a part of paragraph or a document, where coreferencing is common. In this demonstration, we present a system which refines the semantic tuples generated by OpenIE with the aid of a coreference resolution tool. Specifically, all coreferential mentions across the entire document are identified and grouped into coreferential clusters. Objects and subjects in the extracted tuples from OpenIE which match any coreferential mentions are then resolved with a suitable representative term. In this way, our system is able to resolve both anaphoric and cataphoric references, to achieve Document-level OpenIE with Coreference Resolution (DOCoR). The demonstration video can be viewed at https://youtu.be/o9ZSWCBvlDs
DOCoR:具有共同参考分辨率的文档级OpenIE
开放信息提取(Open Information Extraction, OpenIE)从文本中以形式提取关系事实元组。大多数现有的OpenIE解决方案都是在句子级别操作的,并且只从句子中提取关系元组。然而,许多句子作为段落或文档的一部分存在,其中共同引用是常见的。在本演示中,我们提出了一个系统,该系统借助共同引用解析工具对OpenIE生成的语义元组进行细化。具体来说,整个文档中的所有共同引用的提及都被识别并分组到共同引用的集群中。从OpenIE中提取的元组中的对象和主题与任何相关提及相匹配,然后用合适的代表性术语进行解析。通过这种方式,我们的系统能够同时解析回指引用和回指引用,从而实现文档级的DOCoR (Coreference Resolution)。该演示视频可在https://youtu.be/o9ZSWCBvlDs上观看
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