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
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
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
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
Background N-terminal-pro-brain natriuretic peptide(NT-pro-BNP)is associated with worse outcome in patients with acute myocardial infarction(AMI). However,the role of short-term follow-up of NT-pro-BNP level remains u...Background N-terminal-pro-brain natriuretic peptide(NT-pro-BNP)is associated with worse outcome in patients with acute myocardial infarction(AMI). However,the role of short-term follow-up of NT-pro-BNP level remains unclear. Methods Three hundred and sixty-two patients diagnosed with AMI were retrospectively enrolled in this study from March 2014 to March 2017 in our center. Blood samples were obtained at initial admission and again within 1 month after hospital discharge. The univariate and multivariate cox regression analyses including significant covariables were performed on NT-pro-BNP level at admission,discharge,or change from admission to discharge to predict adverse cardiovascular events(MACE)as study endpoints. Results There were 211 cases in NT-pro-BNP decrease group,while 151 cases in NT-pro-BNP increase group. The median follow-up was 365 days(interquartile range[IQR],322-861 days). After adjusting the covariables in the multiple logistic regression analysis,follow-up NT-pro-BNP level was still a significant independent predictor for MACE(OR,1.395;95% CI,1.102-1.869,P=0.005). However,the initial NT-pro-BNP level or change of NT-pro-BNP level had no significant predictive value for MACE. Conclusions A short-term follow-up NT-pro-BNP level after hospital discharge is a powerful prognostic biomarker for MACE in patients with AMI.[S Chin J Cardiol 2019;20(3):168-173]展开更多
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.
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.
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。
文摘Background N-terminal-pro-brain natriuretic peptide(NT-pro-BNP)is associated with worse outcome in patients with acute myocardial infarction(AMI). However,the role of short-term follow-up of NT-pro-BNP level remains unclear. Methods Three hundred and sixty-two patients diagnosed with AMI were retrospectively enrolled in this study from March 2014 to March 2017 in our center. Blood samples were obtained at initial admission and again within 1 month after hospital discharge. The univariate and multivariate cox regression analyses including significant covariables were performed on NT-pro-BNP level at admission,discharge,or change from admission to discharge to predict adverse cardiovascular events(MACE)as study endpoints. Results There were 211 cases in NT-pro-BNP decrease group,while 151 cases in NT-pro-BNP increase group. The median follow-up was 365 days(interquartile range[IQR],322-861 days). After adjusting the covariables in the multiple logistic regression analysis,follow-up NT-pro-BNP level was still a significant independent predictor for MACE(OR,1.395;95% CI,1.102-1.869,P=0.005). However,the initial NT-pro-BNP level or change of NT-pro-BNP level had no significant predictive value for MACE. Conclusions A short-term follow-up NT-pro-BNP level after hospital discharge is a powerful prognostic biomarker for MACE in patients with AMI.[S Chin J Cardiol 2019;20(3):168-173]
