@inproceedings{li-etal-2023-stinmatch,
title = "{STINM}atch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion",
author = "Li, Xurui and
Qin, Yue and
Zhu, Rui and
Lin, Tianqianjin and
Fan, Yongming and
Kang, Yangyang and
Song, Kaisong and
Zhao, Fubang and
Sun, Changlong and
Tang, Haixu and
Liu, Xiaozhong",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.578",
doi = "10.18653/v1/2023.emnlp-main.578",
pages = "9304--9315",
abstract = "Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.",
}
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<abstract>Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.</abstract>
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%0 Conference Proceedings
%T STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion
%A Li, Xurui
%A Qin, Yue
%A Zhu, Rui
%A Lin, Tianqianjin
%A Fan, Yongming
%A Kang, Yangyang
%A Song, Kaisong
%A Zhao, Fubang
%A Sun, Changlong
%A Tang, Haixu
%A Liu, Xiaozhong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-stinmatch
%X Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.
%R 10.18653/v1/2023.emnlp-main.578
%U https://aclanthology.org/2023.emnlp-main.578
%U https://doi.org/10.18653/v1/2023.emnlp-main.578
%P 9304-9315
Markdown (Informal)
[STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion](https://aclanthology.org/2023.emnlp-main.578) (Li et al., EMNLP 2023)
ACL
- Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, and Xiaozhong Liu. 2023. STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9304–9315, Singapore. Association for Computational Linguistics.