As a subtask of information extraction (IE), which aims to extract structured information from texts, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general,...As a subtask of information extraction (IE), which aims to extract structured information from texts, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general, event extraction can be divided into two subtasks: trigger extraction and argument extraction. Currently, the frequent existences of unannotated trigger mentions and poor-context trigger mentions impose critical challenges in Chinese trigger extraction. This paper proposes a novel three-layer joint model to integrate three components in trigger extraction, i.e., trigger identification, event type determination, and event subtype determination. In this way, different kinds of evidence on distinct pseudo samples can be well captured to eliminate the harmful effects of those un-annotated trigger mentions. In addition, this paper introduces various types of linguistically driven constraints on the trigger and argument semantics into the joint model to recover those poor-context trigger mentions. The experimental results show that our joint model significantly outperforms the state-of-the-art Chinese trigger extraction and Chinese event extraction as a whole.展开更多
In this paper, we compare the performance of the optimal attainable payoffs (of a general claim) derived by the variance-optimal approach and the indifference argument under the mean-variance preference in an incomple...In this paper, we compare the performance of the optimal attainable payoffs (of a general claim) derived by the variance-optimal approach and the indifference argument under the mean-variance preference in an incomplete market. Both payoffs are expressed by the signed variance-optimal martingale measure. Our results are applied to the claim hedging under partial information.展开更多
群体研讨支持系统(Group Argument Support Systems,GASS)的匿名、并行输入及自动化记录群体发言的特征,在辅助群体产生大量有价值观点的同时,也常常导致"信息过载"和"知识断层"。介绍了一个自动化聚类工具来增强...群体研讨支持系统(Group Argument Support Systems,GASS)的匿名、并行输入及自动化记录群体发言的特征,在辅助群体产生大量有价值观点的同时,也常常导致"信息过载"和"知识断层"。介绍了一个自动化聚类工具来增强群体的认知能力并提高电子会议的效率。首先识别了GASS环境下自动化主题聚类的一些挑战并回顾了相关研究,结合GASS的研讨模式、研讨文本特征及中文文本分析的要求,给出了中文分词、停词表处理以及有效词语识别的文本分析技术。提出基于主题分析的特征向量选择方法,并基于自组织映射的神经网络思想,用Java语言设计并开发了一个自动聚类工具。实验表明,该工具可以达到0.28的聚类准确率,0.35的聚类全面率,产生0.83的聚类错误率。展开更多
文摘As a subtask of information extraction (IE), which aims to extract structured information from texts, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general, event extraction can be divided into two subtasks: trigger extraction and argument extraction. Currently, the frequent existences of unannotated trigger mentions and poor-context trigger mentions impose critical challenges in Chinese trigger extraction. This paper proposes a novel three-layer joint model to integrate three components in trigger extraction, i.e., trigger identification, event type determination, and event subtype determination. In this way, different kinds of evidence on distinct pseudo samples can be well captured to eliminate the harmful effects of those un-annotated trigger mentions. In addition, this paper introduces various types of linguistically driven constraints on the trigger and argument semantics into the joint model to recover those poor-context trigger mentions. The experimental results show that our joint model significantly outperforms the state-of-the-art Chinese trigger extraction and Chinese event extraction as a whole.
基金This work is supported in part by National Science Fund for Distinguished Young Scholar No. 70225002.
文摘In this paper, we compare the performance of the optimal attainable payoffs (of a general claim) derived by the variance-optimal approach and the indifference argument under the mean-variance preference in an incomplete market. Both payoffs are expressed by the signed variance-optimal martingale measure. Our results are applied to the claim hedging under partial information.
文摘群体研讨支持系统(Group Argument Support Systems,GASS)的匿名、并行输入及自动化记录群体发言的特征,在辅助群体产生大量有价值观点的同时,也常常导致"信息过载"和"知识断层"。介绍了一个自动化聚类工具来增强群体的认知能力并提高电子会议的效率。首先识别了GASS环境下自动化主题聚类的一些挑战并回顾了相关研究,结合GASS的研讨模式、研讨文本特征及中文文本分析的要求,给出了中文分词、停词表处理以及有效词语识别的文本分析技术。提出基于主题分析的特征向量选择方法,并基于自组织映射的神经网络思想,用Java语言设计并开发了一个自动聚类工具。实验表明,该工具可以达到0.28的聚类准确率,0.35的聚类全面率,产生0.83的聚类错误率。
