Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of ...Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks.Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes.However,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and edges.The methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute information.This paper proposes a community discovery algorithm based on multi-relationship embedding.The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder.The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding matrix.This strategy allows capturing of rich structural and attributes information in multi-relational networks.Experiments were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.展开更多
针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)...针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)转换为词向量后,将其与通过图卷积获得的句法依赖信息进行融合,形成更丰富的文本特征;然后通过多头注意力层对文本特征进行加权处理,以此抑制噪声特征的干扰,并促进特征之间的交互,随后根据跨度将文本信息分割成跨度序列进行实体识别;最后使用双向门控循环单元提取局部上下文隐含信息,将与实体类型信息融合到候选实体跨度对并使用sigmoid函数进行关系分类。实验表明,该模型在SciERC数据集和CoNLL04数据集上取得良好的提升效果。展开更多
基金This work was supported by the Key Technologies Research and Development Program of Liaoning Province in China under Grant 2021JH1/10400079the Fundamental Research Funds for the Central Universities under Grant 2217002.
文摘Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks.Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes.However,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and edges.The methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute information.This paper proposes a community discovery algorithm based on multi-relationship embedding.The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder.The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding matrix.This strategy allows capturing of rich structural and attributes information in multi-relational networks.Experiments were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.
文摘针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)转换为词向量后,将其与通过图卷积获得的句法依赖信息进行融合,形成更丰富的文本特征;然后通过多头注意力层对文本特征进行加权处理,以此抑制噪声特征的干扰,并促进特征之间的交互,随后根据跨度将文本信息分割成跨度序列进行实体识别;最后使用双向门控循环单元提取局部上下文隐含信息,将与实体类型信息融合到候选实体跨度对并使用sigmoid函数进行关系分类。实验表明,该模型在SciERC数据集和CoNLL04数据集上取得良好的提升效果。
