@inproceedings{poumay-ittoo-2023-htmot,
title = "{HTMOT}: Hierarchical Topic Modelling over Time",
author = "Poumay, Judicael and
Ittoo, Ashwin",
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-1.92",
pages = "854--863",
abstract = "Topic models provide an efficient way of extracting insights from text and supporting decision-making. Recently, novel methods have been proposed to model topic hierarchy or temporality. Modeling temporality provides more precise topics by separating topics that are characterized by similar words but located over distinct time periods. Conversely, modeling hierarchy provides a more detailed view of the content of a corpus by providing topics and sub-topics. However, no models have been proposed to incorporate both hierarchy and temporality which could be beneficial for applications such as environment scanning. Therefore, we propose a novel method to perform Hierarchical Topic Modelling Over Time (HTMOT). We evaluate the performance of our approach on a corpus of news articles using the Word Intrusion task. Results demonstrate that our model produces topics that elegantly combine a hierarchical structure and a temporal aspect. Furthermore, our proposed Gibbs sampling implementation shows competitive performance compared to previous state-of-the-art methods.",
}
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<abstract>Topic models provide an efficient way of extracting insights from text and supporting decision-making. Recently, novel methods have been proposed to model topic hierarchy or temporality. Modeling temporality provides more precise topics by separating topics that are characterized by similar words but located over distinct time periods. Conversely, modeling hierarchy provides a more detailed view of the content of a corpus by providing topics and sub-topics. However, no models have been proposed to incorporate both hierarchy and temporality which could be beneficial for applications such as environment scanning. Therefore, we propose a novel method to perform Hierarchical Topic Modelling Over Time (HTMOT). We evaluate the performance of our approach on a corpus of news articles using the Word Intrusion task. Results demonstrate that our model produces topics that elegantly combine a hierarchical structure and a temporal aspect. Furthermore, our proposed Gibbs sampling implementation shows competitive performance compared to previous state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T HTMOT: Hierarchical Topic Modelling over Time
%A Poumay, Judicael
%A Ittoo, Ashwin
%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 poumay-ittoo-2023-htmot
%X Topic models provide an efficient way of extracting insights from text and supporting decision-making. Recently, novel methods have been proposed to model topic hierarchy or temporality. Modeling temporality provides more precise topics by separating topics that are characterized by similar words but located over distinct time periods. Conversely, modeling hierarchy provides a more detailed view of the content of a corpus by providing topics and sub-topics. However, no models have been proposed to incorporate both hierarchy and temporality which could be beneficial for applications such as environment scanning. Therefore, we propose a novel method to perform Hierarchical Topic Modelling Over Time (HTMOT). We evaluate the performance of our approach on a corpus of news articles using the Word Intrusion task. Results demonstrate that our model produces topics that elegantly combine a hierarchical structure and a temporal aspect. Furthermore, our proposed Gibbs sampling implementation shows competitive performance compared to previous state-of-the-art methods.
%U https://aclanthology.org/2023.ranlp-1.92
%P 854-863
Markdown (Informal)
[HTMOT: Hierarchical Topic Modelling over Time](https://aclanthology.org/2023.ranlp-1.92) (Poumay & Ittoo, RANLP 2023)
ACL
- Judicael Poumay and Ashwin Ittoo. 2023. HTMOT: Hierarchical Topic Modelling over Time. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 854–863, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.