@inproceedings{71f5fa6327d2469abdb009abddeb9798,
title = "Leveraging meta information in short text aggregation",
abstract = "Analysing topics in short texts (e.g., tweets and new headings) is a challenging task because short texts often contain insufficient word co-occurrence information, which is important to learn good topics in conventional topic topics. 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.",
author = "He Zhao and Lan Du and Guanfeng Liu and Wray Buntine",
note = "Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",
year = "2019",
doi = "10.18653/v1/P19-1396",
language = "English",
isbn = "9781950737482",
series = "ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "4042--4049",
editor = "Anna Korhonen and David Traum and Llu{\'i}s M{\`a}rquez",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
}