Event extraction is one of the most challenging tasks in information extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extr...Event extraction is one of the most challenging tasks in information extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extracting a single event.Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long.In addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error trans-mission.In order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work.Specifically,for multiple event detection,we propose a global-type guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between events.For argument extraction,we converted it into a table-flling problem,and proposed a table-flling method of the attention mechanism,that is simple and can enhance the correlation between trigger words and arguments.The experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6%improvement in the task of event detection,and obtained state-of-the-art results in the argument extraction task,which proved the effectiveness of the method.展开更多
针对现有的类案检索(LCR)方法缺乏对案情要素的有效利用而容易被案例内容的语义结构相似性误导的问题,提出一种融合时序行为链与事件类型的类案检索方法。首先,采取序列标注的方法识别案情描述中的法律事件类型,并利用案例文本中的行为...针对现有的类案检索(LCR)方法缺乏对案情要素的有效利用而容易被案例内容的语义结构相似性误导的问题,提出一种融合时序行为链与事件类型的类案检索方法。首先,采取序列标注的方法识别案情描述中的法律事件类型,并利用案例文本中的行为要素构建时序行为链,以突出案情的关键要素,从而使模型聚焦于案例的核心内容,进而解决现有方法易被案例内容的语义结构相似性误导的问题;其次,利用分段编码构造时序行为链的相似性向量表征矩阵,从而增强案例间行为要素的语义交互;最后,通过聚合评分器,从时序行为链、法律事件类型、犯罪类型这3个角度衡量案例的相关性,从而增加案例匹配得分的合理性。实验结果表明,相较于SAILER(Structure-Aware pre-traIned language model for LEgal case Retrieval)方法,所提方法在LeCaRD(Legal Case Retrieval Dataset)上的P@5值提升了4个百分点、P@10值提升了3个百分点、MAP值提升了4个百分点,而NDCG@30值提升了0.8个百分点。可见,该方法能有效利用案情要素来避免案例内容的语义结构相似性的干扰,并能为类案检索提供可靠的依据。展开更多
基金This work was supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2020JJ4624,2019JJ50655)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.19A020)the National Social Science Fund of China(Grant No.20&ZD047)。
文摘Event extraction is one of the most challenging tasks in information extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extracting a single event.Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long.In addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error trans-mission.In order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work.Specifically,for multiple event detection,we propose a global-type guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between events.For argument extraction,we converted it into a table-flling problem,and proposed a table-flling method of the attention mechanism,that is simple and can enhance the correlation between trigger words and arguments.The experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6%improvement in the task of event detection,and obtained state-of-the-art results in the argument extraction task,which proved the effectiveness of the method.
文摘针对现有的类案检索(LCR)方法缺乏对案情要素的有效利用而容易被案例内容的语义结构相似性误导的问题,提出一种融合时序行为链与事件类型的类案检索方法。首先,采取序列标注的方法识别案情描述中的法律事件类型,并利用案例文本中的行为要素构建时序行为链,以突出案情的关键要素,从而使模型聚焦于案例的核心内容,进而解决现有方法易被案例内容的语义结构相似性误导的问题;其次,利用分段编码构造时序行为链的相似性向量表征矩阵,从而增强案例间行为要素的语义交互;最后,通过聚合评分器,从时序行为链、法律事件类型、犯罪类型这3个角度衡量案例的相关性,从而增加案例匹配得分的合理性。实验结果表明,相较于SAILER(Structure-Aware pre-traIned language model for LEgal case Retrieval)方法,所提方法在LeCaRD(Legal Case Retrieval Dataset)上的P@5值提升了4个百分点、P@10值提升了3个百分点、MAP值提升了4个百分点,而NDCG@30值提升了0.8个百分点。可见,该方法能有效利用案情要素来避免案例内容的语义结构相似性的干扰,并能为类案检索提供可靠的依据。
