As a subtask of open domain event extraction(ODEE),new event type induction aims to discover a set of unseen event types from a given corpus.Existing methods mostly adopt semi-supervised or unsupervised learning to ac...As a subtask of open domain event extraction(ODEE),new event type induction aims to discover a set of unseen event types from a given corpus.Existing methods mostly adopt semi-supervised or unsupervised learning to achieve the goal,which uses complex and different objective functions for labeled and unlabeled data respectively.In order to unify and simplify objective functions,a reliable pseudo-labeling prediction(RPP)framework for new event type induction was proposed.The framework introduces a double label reassignment(DLR)strategy for unlabeled data based on swap-prediction.DLR strategy can alleviate the model degeneration caused by swap-predication and further combine the real distribution over unseen event types to produce more reliable pseudo labels for unlabeled data.The generated reliable pseudo labels help the overall model be optimized by a unified and simple objective.Experiments show that RPP framework outperforms the state-of-the-art on the benchmark.展开更多
Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automa...Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types. Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning.展开更多
Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, seque...Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, sequential Bayesian inference is an example of this mapping formula, and Hori et al. [2] made the mapping formula multidimensional, introduced the concept of time, to Markov (decision) processes in fuzzy events under ergodic conditions, and derived stochastic differential equations in fuzzy events, although in reverse. In this paper, we focus on type 2 fuzzy. First, assuming that Type 2 Fuzzy Events are transformed and mapped onto the state of nature by a quadratic mapping formula that simultaneously considers longitudinal and transverse ambiguity, the joint stochastic differential equation representing these two ambiguities can be applied to possibility principal factor analysis if the weights of the equations are orthogonal. This indicates that the type 2 fuzzy is a two-dimensional possibility multivariate error model with longitudinal and transverse directions. Also, when the weights are oblique, it is a general possibility oblique factor analysis. Therefore, an example of type 2 fuzzy system theory is the possibility factor analysis. Furthermore, we show the initial and stopping condition on possibility factor rotation, on the base of possibility theory.展开更多
目的比较非奈利酮与钠-葡萄糖共转运蛋白-2(sodium-glucose cotransporter-2,SGLT2)抑制剂对2型糖尿病和/或慢性肾脏病患者心血管事件的影响。方法检索PubMed、Cochrane Library、Web of Science和Embase数据库关于2型糖尿病和/或慢性...目的比较非奈利酮与钠-葡萄糖共转运蛋白-2(sodium-glucose cotransporter-2,SGLT2)抑制剂对2型糖尿病和/或慢性肾脏病患者心血管事件的影响。方法检索PubMed、Cochrane Library、Web of Science和Embase数据库关于2型糖尿病和/或慢性肾脏病患者的随机对照试验,时间为建库至2023年7月3日。基于频率模型,使用STATA 17.0软件进行网状荟萃分析(network meta-analysis,NMA)。结果共纳入7项随机对照试验,包括33206例患者。涉及的治疗方式包括非奈利酮和SGLT2抑制剂,其中SGLT2抑制剂包含恩格列净、卡格列净、达格列净和索格列净(双重SGLT抑制剂)。在心血管复合事件方面,根据累计曲线下的概率面积(surface under the cumulative ranking area,SUCRA)排序,索格列净最有效。在心血管死亡方面,根据SUCRA排序,恩格列净最有效。在心力衰竭住院方面,根据SUCRA排序,卡格列净最有效。在全因死亡方面,根据SUCRA排序,达格列净最有效。非奈利酮和SGLT2抑制剂在不良事件、严重不良事件和急性肾损害的安全性方面比较,差异均无统计学意义(均P>0.05)。与采用非奈利酮治疗的患者相比,采用SGLT2抑制剂治疗的患者高钾血症发生率更低(RR=0.41,95%CI 0.32~0.52)。结论与非奈利酮相比,SGLT2抑制剂能更好地降低心血管事件的发生率,可作为2型糖尿病和/或慢性肾脏病患者的基础治疗,帮助预防或减少心血管事件。展开更多
利用山东省84个气象台站的逐日降水资料、美国国家环境预测中心和国家大气研究中心(National Centers for Environmental Prediction and National Center for Atmospheric Research, NCEP/NCAR)逐日再分析资料以及中国气象局(CMA)热带...利用山东省84个气象台站的逐日降水资料、美国国家环境预测中心和国家大气研究中心(National Centers for Environmental Prediction and National Center for Atmospheric Research, NCEP/NCAR)逐日再分析资料以及中国气象局(CMA)热带气旋资料中心的CMA最佳路径数据集,对1969—2020年夏季(6—8月)发生在山东的857例极端暴雨事件(Extreme rainstorm events, EREs)的时空分布特征及影响环流分型进行了分析。结果表明:山东夏季EREs主要集中在7和8月,8月极端暴雨降水量占当月总降水量的比值最大,可达53.5%。山东夏季极端暴雨降水量以及极端暴雨发生日数呈现不显著的增加趋势,8月的增加趋势最明显。使用经验正交函数分解对影响山东夏季EREs的大气环流系统进行分型,发现影响山东夏季出现EREs的环流系统主要有4类,其中,影响ERE最多的环流系统是北方气旋型,约占事件总频次的33.1%;其次是高空急流型,占比约11.3%;南方气旋型和热带气旋型的环流型影响相当,出现的概率分别为9.7%和9.4%。展开更多
基金supported by the National Natural Science Foundation of China(62076031)。
文摘As a subtask of open domain event extraction(ODEE),new event type induction aims to discover a set of unseen event types from a given corpus.Existing methods mostly adopt semi-supervised or unsupervised learning to achieve the goal,which uses complex and different objective functions for labeled and unlabeled data respectively.In order to unify and simplify objective functions,a reliable pseudo-labeling prediction(RPP)framework for new event type induction was proposed.The framework introduces a double label reassignment(DLR)strategy for unlabeled data based on swap-prediction.DLR strategy can alleviate the model degeneration caused by swap-predication and further combine the real distribution over unseen event types to produce more reliable pseudo labels for unlabeled data.The generated reliable pseudo labels help the overall model be optimized by a unified and simple objective.Experiments show that RPP framework outperforms the state-of-the-art on the benchmark.
基金Supported by the National Natural Science Foundation of China(Nos. 60975055 and 60803093)the National High-Tech Research and Development (863) Program of China (No.2008AA01Z144)
文摘Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types. Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning.
文摘Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, sequential Bayesian inference is an example of this mapping formula, and Hori et al. [2] made the mapping formula multidimensional, introduced the concept of time, to Markov (decision) processes in fuzzy events under ergodic conditions, and derived stochastic differential equations in fuzzy events, although in reverse. In this paper, we focus on type 2 fuzzy. First, assuming that Type 2 Fuzzy Events are transformed and mapped onto the state of nature by a quadratic mapping formula that simultaneously considers longitudinal and transverse ambiguity, the joint stochastic differential equation representing these two ambiguities can be applied to possibility principal factor analysis if the weights of the equations are orthogonal. This indicates that the type 2 fuzzy is a two-dimensional possibility multivariate error model with longitudinal and transverse directions. Also, when the weights are oblique, it is a general possibility oblique factor analysis. Therefore, an example of type 2 fuzzy system theory is the possibility factor analysis. Furthermore, we show the initial and stopping condition on possibility factor rotation, on the base of possibility theory.
文摘目的比较非奈利酮与钠-葡萄糖共转运蛋白-2(sodium-glucose cotransporter-2,SGLT2)抑制剂对2型糖尿病和/或慢性肾脏病患者心血管事件的影响。方法检索PubMed、Cochrane Library、Web of Science和Embase数据库关于2型糖尿病和/或慢性肾脏病患者的随机对照试验,时间为建库至2023年7月3日。基于频率模型,使用STATA 17.0软件进行网状荟萃分析(network meta-analysis,NMA)。结果共纳入7项随机对照试验,包括33206例患者。涉及的治疗方式包括非奈利酮和SGLT2抑制剂,其中SGLT2抑制剂包含恩格列净、卡格列净、达格列净和索格列净(双重SGLT抑制剂)。在心血管复合事件方面,根据累计曲线下的概率面积(surface under the cumulative ranking area,SUCRA)排序,索格列净最有效。在心血管死亡方面,根据SUCRA排序,恩格列净最有效。在心力衰竭住院方面,根据SUCRA排序,卡格列净最有效。在全因死亡方面,根据SUCRA排序,达格列净最有效。非奈利酮和SGLT2抑制剂在不良事件、严重不良事件和急性肾损害的安全性方面比较,差异均无统计学意义(均P>0.05)。与采用非奈利酮治疗的患者相比,采用SGLT2抑制剂治疗的患者高钾血症发生率更低(RR=0.41,95%CI 0.32~0.52)。结论与非奈利酮相比,SGLT2抑制剂能更好地降低心血管事件的发生率,可作为2型糖尿病和/或慢性肾脏病患者的基础治疗,帮助预防或减少心血管事件。
文摘利用山东省84个气象台站的逐日降水资料、美国国家环境预测中心和国家大气研究中心(National Centers for Environmental Prediction and National Center for Atmospheric Research, NCEP/NCAR)逐日再分析资料以及中国气象局(CMA)热带气旋资料中心的CMA最佳路径数据集,对1969—2020年夏季(6—8月)发生在山东的857例极端暴雨事件(Extreme rainstorm events, EREs)的时空分布特征及影响环流分型进行了分析。结果表明:山东夏季EREs主要集中在7和8月,8月极端暴雨降水量占当月总降水量的比值最大,可达53.5%。山东夏季极端暴雨降水量以及极端暴雨发生日数呈现不显著的增加趋势,8月的增加趋势最明显。使用经验正交函数分解对影响山东夏季EREs的大气环流系统进行分型,发现影响山东夏季出现EREs的环流系统主要有4类,其中,影响ERE最多的环流系统是北方气旋型,约占事件总频次的33.1%;其次是高空急流型,占比约11.3%;南方气旋型和热带气旋型的环流型影响相当,出现的概率分别为9.7%和9.4%。
文摘目的:探讨不同年龄段住院2型糖尿病(type 2 diabetes mellitus,T2DM)患者临床特征及心血管事件发生影响因素。方法:收集2019年1月至12月石家庄市第二医院108例住院T2DM患者的临床资料,根据心血管事件发生分为发生组和未发生组,比较两组临床特征及不同年龄住院T2DM患者临床特征,采用Logistic回归方程分析住院T2DM患者心血管事件发生影响因素。结果:(1)所有患者均获得2年随访结果,心血管事件发生率为37.04%;(2)发生组年龄、T2DM病程、糖化血红蛋白(glycosylated hemoglobin,HbA1c)、半乳糖凝集素-3(galectin-3,Gal-3)、高血压病史均大或长或高于未发生组,踝肱指数、C1q/肿瘤坏死因子相关蛋白9(C1q/tumor necrosis factor related protein 9,CTRP9)低于未发生组(P<0.05);(3)老年组T2DM病程、HbA1c、Gal-3、高血压病史均大或长或高于中青年组,踝肱指数、CTRP9低于中青年组(P<0.05);(4)T2DM病程、踝肱指数、年龄、CTRP9是住院T2DM患者心血管事件发生影响因素(P<0.05)。结论:T2DM病程、踝肱指数、年龄、CTRP9可能是导致住院T2DM患者心血管事件发生的影响因素,临床实际中应密切关注伴有上述情况的住院T2DM患者,积极防治,促进预后改善。
