Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual inte...Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents.These interactions are critical to trajectory prediction accuracy.While prior studies have employed Convolutional Neural Networks(CNNs)and Graph Convolutional Networks(GCNs)to model such interactions,these methods fail to distinguish varying influence levels among neighboring pedestrians.To address this,we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions.Specifically,we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians.These features are then fused and processed by the Bidirectional Graph Attention Network(Bi-GAT),which models the bidirectional interactions between the target pedestrian and its neighbors.The model computes node attention weights(i.e.,similarity scores)to differentially aggregate neighbor information,enabling fine-grained interaction representations.Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-theartmethods regarding Average Displacement Error(ADE)and Final Displacement Error(FDE),highlighting its strong prediction accuracy and generalization capability.展开更多
With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service respons...With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.展开更多
传统意图识别模型通常依赖大规模战斗数据进行建模与训练,但在电子对抗与隐身技术不断发展的背景下,情报信息获取受限,使基于大样本的数据驱动方法面临适应性不足的问题。针对上述挑战,引入小样本学习思想至意图识别研究中,将任务建模...传统意图识别模型通常依赖大规模战斗数据进行建模与训练,但在电子对抗与隐身技术不断发展的背景下,情报信息获取受限,使基于大样本的数据驱动方法面临适应性不足的问题。针对上述挑战,引入小样本学习思想至意图识别研究中,将任务建模为监督式消息传递过程,构建一种融合双向长短期记忆(Bidirectional Long Short-term Memory,BiLSTM)网络与部分可观测图模型的端到端深度学习架构。利用BiLSTM网络从有限兵棋态势信息中提取关键时序特征,刻画动态演化规律,并在此基础上构建加权图结构,通过图卷积实现节点特征更新与关系建模,最终完成意图判别。基于兵棋推演平台,在不同情报完备度条件下开展在线意图识别实验,并对比分析特征提取器配置对识别性能的影响。实验结果表明,在数据稀缺场景下,该模型仍具备良好的识别精度与鲁棒性,整体性能优于多种典型小样本学习模型,体现了其在智能指挥与决策支持中的应用潜力。展开更多
随着分布式新能源、可控资源等新型元素接入配电网,传统状态估计模型面临量测信息不全、配电网拓扑变化频繁和负荷时序性波动等新问题,模型估计精度降低。针对该问题,文中提出一种融合改进生成对抗与图注意力网络的配电网状态估计方法...随着分布式新能源、可控资源等新型元素接入配电网,传统状态估计模型面临量测信息不全、配电网拓扑变化频繁和负荷时序性波动等新问题,模型估计精度降低。针对该问题,文中提出一种融合改进生成对抗与图注意力网络的配电网状态估计方法。首先,选取不同的历史时间断面,利用拓扑参数和量测信息生成数据集,通过将双向长短期记忆网络引入生成对抗网络填补数据中的缺失量测信息;其次,利用图注意力网络自适应地捕捉节点间的空间动态关系,利用双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络充分挖掘不同时间断面序列信息的时间耦合关系,拼接形成关于量测量到状态量的时空特征表达,得到改进图神经网络状态估计模型;最后,在IEEE 118节点系统中进行仿真实验,并与卷积神经网络、图注意力网络等算法进行对比。结果表明,文中所提算法在数据缺失和拓扑时变情况下具有更优的估计效果。展开更多
Drug-target interaction(DTI)is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery.However,the traditional bio-experimental process of drug-target interaction identificati...Drug-target interaction(DTI)is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery.However,the traditional bio-experimental process of drug-target interaction identification requires a large investment of time and labor.To address this challenge,graph neural network(GNN)approaches in deep learning are becoming a prominent trend in the field of DTI research,which is characterized by multimodal processing of data,feature learning and interpretability in DTI.Nevertheless,some methods are still limited by homogeneous graphs and single features.To address the problems,we mechanistically analyze graph convolutional neural networks(GCNs)and graph attentional neural networks(GATs)to propose a new model for the prediction of drug-target interactions using graph neural networks named BiTGNN[Bidirectional Transformer(Bi-Transformer)-graph neural network].The method first establishes drug-target pairs through the pseudo-position specificity scoring matrix(PsePSSM)and drug fingerprint data,and constructs a heterogeneous network by utilizing the relationship between the drug and the target.Then,the computational extraction of drug and target attributes is performed using GCNs and GATs for the purpose of model information flow extension and graph information enhancement.We collect interaction data using the proposed Bi-Transformer architecture,in which we design a bidirectional cross-attention mechanism for calculating the effects of drugtarget interactions for realistic biological interaction simulations.Finally,a feed-forward neural network is used to obtain the feature matrices of the drug and the target,and DTI prediction is performed by fusing the two feature matrices.The Enzyme,Ion Channel(IC),G Protein-coupled Receptor(GPCR)and Nuclear Receptor(NR)datasets are used in the experiments,and compared with several existing mainstream models,our model outperforms in Area Under the ROC Curve(AUC),Specificity,Accuracy and the metric Area Under the Precision-Recall Curve(AUPR).展开更多
In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,ha...In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.展开更多
This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high di...This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.展开更多
Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtempora...Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.展开更多
Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic...Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.展开更多
The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Curr...The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.展开更多
For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net...For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.展开更多
基金funded by the National Natural Science Foundation of China,grant number 624010funded by the Natural Science Foundation of Anhui Province,grant number 2408085QF202+1 种基金funded by the Anhui Future Technology Research Institute Industry Guidance Fund Project,grant number 2023cyyd04funded by the Project of Research of Anhui Polytechnic University,grant number Xjky2022150.
文摘Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents.These interactions are critical to trajectory prediction accuracy.While prior studies have employed Convolutional Neural Networks(CNNs)and Graph Convolutional Networks(GCNs)to model such interactions,these methods fail to distinguish varying influence levels among neighboring pedestrians.To address this,we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions.Specifically,we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians.These features are then fused and processed by the Bidirectional Graph Attention Network(Bi-GAT),which models the bidirectional interactions between the target pedestrian and its neighbors.The model computes node attention weights(i.e.,similarity scores)to differentially aggregate neighbor information,enabling fine-grained interaction representations.Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-theartmethods regarding Average Displacement Error(ADE)and Final Displacement Error(FDE),highlighting its strong prediction accuracy and generalization capability.
基金supported by Science and Technology Project of State Grid Corporation(Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data)
文摘With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.
文摘传统意图识别模型通常依赖大规模战斗数据进行建模与训练,但在电子对抗与隐身技术不断发展的背景下,情报信息获取受限,使基于大样本的数据驱动方法面临适应性不足的问题。针对上述挑战,引入小样本学习思想至意图识别研究中,将任务建模为监督式消息传递过程,构建一种融合双向长短期记忆(Bidirectional Long Short-term Memory,BiLSTM)网络与部分可观测图模型的端到端深度学习架构。利用BiLSTM网络从有限兵棋态势信息中提取关键时序特征,刻画动态演化规律,并在此基础上构建加权图结构,通过图卷积实现节点特征更新与关系建模,最终完成意图判别。基于兵棋推演平台,在不同情报完备度条件下开展在线意图识别实验,并对比分析特征提取器配置对识别性能的影响。实验结果表明,在数据稀缺场景下,该模型仍具备良好的识别精度与鲁棒性,整体性能优于多种典型小样本学习模型,体现了其在智能指挥与决策支持中的应用潜力。
文摘随着分布式新能源、可控资源等新型元素接入配电网,传统状态估计模型面临量测信息不全、配电网拓扑变化频繁和负荷时序性波动等新问题,模型估计精度降低。针对该问题,文中提出一种融合改进生成对抗与图注意力网络的配电网状态估计方法。首先,选取不同的历史时间断面,利用拓扑参数和量测信息生成数据集,通过将双向长短期记忆网络引入生成对抗网络填补数据中的缺失量测信息;其次,利用图注意力网络自适应地捕捉节点间的空间动态关系,利用双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络充分挖掘不同时间断面序列信息的时间耦合关系,拼接形成关于量测量到状态量的时空特征表达,得到改进图神经网络状态估计模型;最后,在IEEE 118节点系统中进行仿真实验,并与卷积神经网络、图注意力网络等算法进行对比。结果表明,文中所提算法在数据缺失和拓扑时变情况下具有更优的估计效果。
基金supported by the National Key R&D Program of China under the Project No.2021YFB2802300National Natural Science Foundation of China under the Grant Nos.12271362 and 12061059.
文摘Drug-target interaction(DTI)is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery.However,the traditional bio-experimental process of drug-target interaction identification requires a large investment of time and labor.To address this challenge,graph neural network(GNN)approaches in deep learning are becoming a prominent trend in the field of DTI research,which is characterized by multimodal processing of data,feature learning and interpretability in DTI.Nevertheless,some methods are still limited by homogeneous graphs and single features.To address the problems,we mechanistically analyze graph convolutional neural networks(GCNs)and graph attentional neural networks(GATs)to propose a new model for the prediction of drug-target interactions using graph neural networks named BiTGNN[Bidirectional Transformer(Bi-Transformer)-graph neural network].The method first establishes drug-target pairs through the pseudo-position specificity scoring matrix(PsePSSM)and drug fingerprint data,and constructs a heterogeneous network by utilizing the relationship between the drug and the target.Then,the computational extraction of drug and target attributes is performed using GCNs and GATs for the purpose of model information flow extension and graph information enhancement.We collect interaction data using the proposed Bi-Transformer architecture,in which we design a bidirectional cross-attention mechanism for calculating the effects of drugtarget interactions for realistic biological interaction simulations.Finally,a feed-forward neural network is used to obtain the feature matrices of the drug and the target,and DTI prediction is performed by fusing the two feature matrices.The Enzyme,Ion Channel(IC),G Protein-coupled Receptor(GPCR)and Nuclear Receptor(NR)datasets are used in the experiments,and compared with several existing mainstream models,our model outperforms in Area Under the ROC Curve(AUC),Specificity,Accuracy and the metric Area Under the Precision-Recall Curve(AUPR).
基金Supported by the Science and Technological Tackling Project of Heilongjiang Province(GB06A106)
文摘In the past 30 years,signed directed graph(SDG) ,one of the qualitative simulation technologies,has been widely applied for chemical fault diagnosis.However,SDG based fault diagnosis,as any other qualitative method,has poor diagnostic resolution.In this paper,a new method that combines SDG with qualitative trend analysis(QTA) is presented to improve the resolution.In the method,a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model.Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consis-tent paths found in the previous step.New consistency rules based on qualitative trends are used to find the real causes from the candidate causes.The resolution can be improved.This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA.The implementation of SDG-QTA based fault diagno-sis is done using the integrated SDG modeling,inference and post-processing software platform.Its application is illustrated on an atmospheric distillation tower unit of a simulation platform.The result shows its good applicability and efficiency.
基金supported by the National Natural Science Foundation of China(Grant No.61375030)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20090149)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province,China(Grant No.08KJD520019)
文摘This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.
基金supported by the Key Research&Development Plan Project of Shandong Province,China(No.2017GGX10127).
文摘Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.
基金the National Natural Science Foundation of China(No.61461027,61762059)the Provincial Science and Technology Program supported the Key Project of Natural Science Foundation of Gansu Province(No.22JR5RA226)。
文摘Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.
文摘The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.
基金Supported by the National Key Research and Development Program of China(No.2018YFB1702601).
文摘For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.
