摘要
事件抽取是信息抽取领域的研究热点。针对ACE事件抽取局限于当前单个句子而造成大量事件论元角色缺失的现象,提出了基于跨事件的缺失事件角色填充理论并实现了原型系统。系统分为缺失角色填充识别和缺失角色填充分类两个部分,识别部分用于判定缺失角色是否可被填充,分类部分用于从其它事件描述中选择合适的角色(实体)对可被填充的缺失角色进行填充。对ACE2005语料进行了后期标注,实验中两个阶段的F值分别达到72.97和74.68。
Event extraction is an important research direction in the area of information extraction. In response to the phenomenon that a large number of arguments are missing in ACE event extraction caused by focusing on the current single sentence, we proposed a theory of filling the missing event arguments based on cross-event inference and achieved the prototype system. The system is divided into two parts that are identification and classification of missing roles. I- dentification part is applied to decide whether a missing event argument can be filled while classification part is applied to decide which argument in other event mention can be used to fill the missing event argument. We annotated ACE 2005 corpus to reveal the filling relationships. The experimental results show that the F measure reaches 72.97 and 74. 68 respectively.
出处
《计算机科学》
CSCD
北大核心
2012年第7期200-204,共5页
Computer Science
基金
国家自然科学基金(90920004
60970056
61070123
61003153)
江苏省高校自然科学重大基础研究项目(08KJA520002)资助
关键词
角色填充
跨事件
事件抽取
Filling of missing event argument,Cross-event inference, Event extraction
