基于外联关系的隐式篇章关系推理

Implicit Discourse Relation Inference Based on the External Relation

针对隐式篇章关系(implicit discourse relation)分类性能较低的问题,提出一种基于"外联"关系的无监督隐式篇章关系推理方法.该方法继承"显式指导隐式"的关系推理模式,针对每个待测"论元对",在大规模外部数据资源中挖掘与其内容近似的显式"参考对",借助"参考对"的显式关系推理隐式关系.特别地,该方法侧重挖掘2个论元中能够协同触发篇章关系的文字片段(即"外联"成分),以"外联"成分间的关系为参考,推理"论元对"整体的篇章关系.利用宾州篇章树库(Penn discourse treebank,PDTB)对这一推理方法进行评测.实验结果显示,该方法在隐式篇章关系推理性能上获得显著提升,识别精确率达到54.12%,与现有主流推理方法性能对比,识别精确率提升11.82%.

The main task of the discourse relation recognition is to automatically identify the relationships between two text spans.Currently,the performance of the explicit discourse relation can reach 93.09% because of its direct clues,however the performance of the implicit discourse recognition is still far from satisfactory since it has no direct clues.For the resolution of this issue,this paper proposes a novel implicit discourse relation inference approach based on the external relation.The method follows the existing inference pattern that uses the explicit relation to infer the implicit relation.Firstly,it searches the explicit reference arguments that have the similar content with the test arguments in the large scale of external data,then it uses the standard sorting algorithm to rank the explicit reference arguments.Finally,it predicts the implicit discourse relation based on the ranking results.Especially,the method focuses on mining the text fragments which can synergistically trigger the discourse relation between two arguments（called external elements）,and predicts the implicit discourse relation of the arguments with reference to the relation between two external elements.Experiments on the Penn discourse treebank（PDTB）show an accuracy of54.12%,which is a significant improvement of 11.82% over the current state-of-the-art system.