基于相似义原和依存句法的政外领域事件抽取方法

Event extraction in political diplomacy based on similar semantics and dependency syntax

以政外领域新闻数据为研究基础,针对基于传统模式匹配事件抽取存在的提取困难、召回率和准确率低,基于深度学习方法在特定领域事件抽取中抽取准确率不高等问题,提出基于相似义原和依存句法的政外领域事件抽取方法。通过计算义原描述式的相似性,扩展事件触发词表,为精准识别事件类型奠定基础;进一步基于模式的指导,结合文本依存句法分析实现对于政外领域事件元素的识别和抽取,从而达到对事件的结构化描述。抽取结果准确率明显优于基于深度神经网络的端到端事件抽取模型抽取结果,并对其他特定领域事件抽取具有可借鉴性和实施性。最后对事件抽取面临的主要困难和应用前景进行了探讨和总结。

Based on the research of news data in the field of political diplomacy,aiming at the pro-blems of extraction difficulty,low recall rate and accuracy rate of event extraction based on traditional pattern matching,and low accuracy rate of event extraction based on deep learning method in specific field,this paper proposes an event extraction method in the field of political diplomacy based on similar semantics and dependency syntax.The proposed method extends the event triggers by similarity calculation of semantic description,which lays a foundation for accurate recognition of event types.Furthermore,based on patterns,text dependency syntactic parsing is used to identify and extract event elements in political and diplomatic fields,so as to achieve a structured description of events.The accuracy of the extraction results is obviously better than that of the end-to-end event extraction model based on the deep neural network,and it can be used for reference and implementation in other specific fields.Finally,the main difficulties and application prospects of event extraction are discussed and summarized.