@inproceedings{liang-etal-2023-classification,
title = "Classification-Aware Neural Topic Model Combined with Interpretable Analysis - for Conflict Classification",
author = "Liang, Tianyu and
Mu, Yida and
Kim, Soonho and
Kuate, Darline and
Lang, Julie and
Vos, Rob and
Song, Xingyi",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.72",
pages = "666--672",
abstract = "A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information Classification and Topic Discovery. The model provides a reliable interpretation of classification results and discovered topics by introducing interpretability analysis. At the same time, interpretation is introduced into the model architecture to improve the classification performance of the model and to allow interpretation to focus further on the details of the data. Finally, the model architecture is optimised to reduce the complexity of the model.",
}
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<abstract>A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information Classification and Topic Discovery. The model provides a reliable interpretation of classification results and discovered topics by introducing interpretability analysis. At the same time, interpretation is introduced into the model architecture to improve the classification performance of the model and to allow interpretation to focus further on the details of the data. Finally, the model architecture is optimised to reduce the complexity of the model.</abstract>
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%0 Conference Proceedings
%T Classification-Aware Neural Topic Model Combined with Interpretable Analysis - for Conflict Classification
%A Liang, Tianyu
%A Mu, Yida
%A Kim, Soonho
%A Kuate, Darline
%A Lang, Julie
%A Vos, Rob
%A Song, Xingyi
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F liang-etal-2023-classification
%X A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information Classification and Topic Discovery. The model provides a reliable interpretation of classification results and discovered topics by introducing interpretability analysis. At the same time, interpretation is introduced into the model architecture to improve the classification performance of the model and to allow interpretation to focus further on the details of the data. Finally, the model architecture is optimised to reduce the complexity of the model.
%U https://aclanthology.org/2023.ranlp-1.72
%P 666-672
Markdown (Informal)
[Classification-Aware Neural Topic Model Combined with Interpretable Analysis - for Conflict Classification](https://aclanthology.org/2023.ranlp-1.72) (Liang et al., RANLP 2023)
ACL
- Tianyu Liang, Yida Mu, Soonho Kim, Darline Kuate, Julie Lang, Rob Vos, and Xingyi Song. 2023. Classification-Aware Neural Topic Model Combined with Interpretable Analysis - for Conflict Classification. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 666–672, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.