Papers with heterogeneous information network
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media (2022.naacl-main)
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| Challenge: | Existing approaches focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. |
| Approach: | They propose a political perspective detection approach that leverages news text to enable multi-hop knowledge reasoning and incorporates textual cues as paragraph-level labels. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two benchmark datasets. |
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (D19-1)
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| Challenge: | Existing studies on short text classification focus on long texts and achieve unsatisfactory performance due to the sparsity and limited labeled data. |
| Approach: | They propose a heterogeneous graph neural network based method for semi-supervised short text classification that leverages the full advantage of few labeled data and large unlabeled data through information propagation along the graph. |
| Outcome: | The proposed method outperforms state-of-the-art methods across six benchmark datasets significantly. |
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)
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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
| Challenge: | Existing approaches focus on textual data and voting records to induce political actors' stances. |
| Approach: | They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances. |
| Outcome: | The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection. |