@inproceedings{zhao-etal-2019-leveraging,
title = "Leveraging Meta Information in Short Text Aggregation",
author = "Zhao, He and
Du, Lan and
Liu, Guanfeng and
Buntine, Wray",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1396",
doi = "10.18653/v1/P19-1396",
pages = "4042--4049",
abstract = "Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.",
}
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%0 Conference Proceedings
%T Leveraging Meta Information in Short Text Aggregation
%A Zhao, He
%A Du, Lan
%A Liu, Guanfeng
%A Buntine, Wray
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhao-etal-2019-leveraging
%X Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.
%R 10.18653/v1/P19-1396
%U https://aclanthology.org/P19-1396
%U https://doi.org/10.18653/v1/P19-1396
%P 4042-4049
Markdown (Informal)
[Leveraging Meta Information in Short Text Aggregation](https://aclanthology.org/P19-1396) (Zhao et al., ACL 2019)
ACL
- He Zhao, Lan Du, Guanfeng Liu, and Wray Buntine. 2019. Leveraging Meta Information in Short Text Aggregation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4042–4049, Florence, Italy. Association for Computational Linguistics.