提出了采用基于Pair-Copula分解的藤Copula理论建立多元风速相依模型的方法。该方法首先考虑了风速分布的随机性,并计及风电场内部风机群风速间的相关性,采用Canonical藤描述Pair-Copula分解的逻辑结构,通过求解Canonical藤结构中的Pair...提出了采用基于Pair-Copula分解的藤Copula理论建立多元风速相依模型的方法。该方法首先考虑了风速分布的随机性,并计及风电场内部风机群风速间的相关性,采用Canonical藤描述Pair-Copula分解的逻辑结构,通过求解Canonical藤结构中的Pair-Copula概率密度函数PDF(probabilitydensity function),得到高维联合分布下的Pair-Copula多元风速相依模型;再对某实际风电场进行实证分析,得到了风电场内部6个风机群间风速的Pair-Copula联合概率密度函数JPDF(joint probability density function);最后在风电场风速相关结构的问题上进一步研究分析,为下一步建立混合Copula函数模型提供思路。展开更多
为基于真实语料进行句法分析,构建了大规模的短语结构树库和依存结构树库,并尝试在两种结构的树库之间进行转换.讨论了宾州中文树库(Penn Chinese Treebank,CTB)中短语结构树库和依存结构树库的关系,并基于现代中文依存文法制定了中心...为基于真实语料进行句法分析,构建了大规模的短语结构树库和依存结构树库,并尝试在两种结构的树库之间进行转换.讨论了宾州中文树库(Penn Chinese Treebank,CTB)中短语结构树库和依存结构树库的关系,并基于现代中文依存文法制定了中心子节点过滤表,依据该表将短语结构的CTB转换为依存结构树库.在CTB中随机抽取200句语料,转换正确率达到了99.50%.基于该转换得到的依存结构树库可以进一步进行中文依存关系解析的研究.展开更多
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,...Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.展开更多
文摘提出了采用基于Pair-Copula分解的藤Copula理论建立多元风速相依模型的方法。该方法首先考虑了风速分布的随机性,并计及风电场内部风机群风速间的相关性,采用Canonical藤描述Pair-Copula分解的逻辑结构,通过求解Canonical藤结构中的Pair-Copula概率密度函数PDF(probabilitydensity function),得到高维联合分布下的Pair-Copula多元风速相依模型;再对某实际风电场进行实证分析,得到了风电场内部6个风机群间风速的Pair-Copula联合概率密度函数JPDF(joint probability density function);最后在风电场风速相关结构的问题上进一步研究分析,为下一步建立混合Copula函数模型提供思路。
文摘为基于真实语料进行句法分析,构建了大规模的短语结构树库和依存结构树库,并尝试在两种结构的树库之间进行转换.讨论了宾州中文树库(Penn Chinese Treebank,CTB)中短语结构树库和依存结构树库的关系,并基于现代中文依存文法制定了中心子节点过滤表,依据该表将短语结构的CTB转换为依存结构树库.在CTB中随机抽取200句语料,转换正确率达到了99.50%.基于该转换得到的依存结构树库可以进一步进行中文依存关系解析的研究.
文摘Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.
