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| Publications [#349653] of Lawrence Carin
Papers Published
- Chen, L; Wang, G; Tao, C; Shen, D; Cheng, P; Zhang, X; Wang, W; Zhang, Y; Carin, L, Improving textual network embedding with global attention via optimal transport,
ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
(January, 2020),
pp. 5193-5202, ISBN 9781950737482
(last updated on 2024/12/31)
Abstract: Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network-embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport, and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.
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