This study presents a novel approach for disaggregating aggregated train delays into primary and secondary components using gated graph convolutional networks (gatedGCNs). We develop a graph-based representation of ra...
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This study presents a novel approach for disaggregating aggregated train delays into primary and secondary components using gated graph convolutional networks (gatedGCNs). We develop a graph-based representation of railway traffic that captures complex spatiotemporal relationships and long-range dependencies. Our method is applied to synthetic delay data generated from an agent-based simulation model of the Austrian railway network. We evaluate the model on classification and regression tasks, demonstrating high accuracy in distinguishing between primary and secondary delays. The classification task achieves 96% accuracy and 0.99 AUC, while the regression task attains an R-squared value of 0.86. These results significantly outperform a naive baseline model. The findings suggest that gatedGCN is a promising method for delay disaggregation and has potential for broader applications in capturing delay propagation processes. However, while the results on synthetic data demonstrate strong performance, further validation on real-world data is essential to confirm its practical applicability.
Aspect-based sentiment classification aims to predict the sentiment polarity of specific aspects appeared in a sentence. Nowadays, most current methods mainly focus on the semantic information by exploiting traditiona...
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Aspect-based sentiment classification aims to predict the sentiment polarity of specific aspects appeared in a sentence. Nowadays, most current methods mainly focus on the semantic information by exploiting traditional attention mechanisms combined with recurrent neural networks to capture the interaction between the contexts and the targets. However, these models did not consider the importance of the relevant syntactical constraints. In this paper, we propose to employ a novel gated graph convolutional networks on the dependency tree to encode syntactical information, and we design a Syntax-aware Context Dynamic Weighted layer to guide our model to pay more attention to the local syntax-aware context. Moreover, Multi-head Attention is utilized for capturing both semantic information and interactive information between semantics and syntax. We conducted experiments on five datasets and the results demonstrate the effectiveness of the proposed model.
Multi-turn response selection is an important task in natural language processing, which is designed for developing dialogue agents. Existing models on this task mainly extract semantic features of dialogue contexts a...
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ISBN:
(纸本)9780738133669
Multi-turn response selection is an important task in natural language processing, which is designed for developing dialogue agents. Existing models on this task mainly extract semantic features of dialogue contexts and rely heavily on linguistic matching for response selection. However, these previous approaches simply consider contextual features and largely ignore the temporal information among utterances. In this paper, we propose a novel graph-based retrieval model to tackle the above problems. We first construct a temporal graph based on both dialogue contexts and utterance relations, and then leverage the gated graph convolutional networks to aggregate significant information from all neighboring utterances. Preciously, we exploit the proposed graph-based architecture to perform accurate reasoning over multi-turn dialogues, capturing semantic and temporal features simultaneously for selecting the appropriate response. Experimental results have shown that our model can achieve strong performance on multi-turn response selection compared to the baseline models. Additionally, ablation studies validate the effectiveness of different components in our model.
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