Hierarchical lifelong topic modeling using rules extracted from network communities.
Topic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0264481&type=printable |
| _version_ | 1827096450823094272 |
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| author | Muhammad Taimoor Khan Nouman Azam Shehzad Khalid Furqan Aziz |
| author_facet | Muhammad Taimoor Khan Nouman Azam Shehzad Khalid Furqan Aziz |
| author_sort | Muhammad Taimoor Khan |
| collection | DOAJ |
| description | Topic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and introduce hierarchical lifelong topic models. Hierarchical lifelong topic models not only allow to examine the topics at different levels of granularity but also allows to continuously adjust the granularity of the topics as more information becomes available. A fundamental issue in hierarchical lifelong topic modeling is the extraction of rules that are used to preserve the hierarchical structural information among the rules and will continuously update based on new information. To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. Experimental results indicate improvement of the hierarchical topic structures in terms of topic coherence that increases from general to specific topics. |
| first_indexed | 2024-03-11T12:49:46Z |
| format | Article |
| id | doaj.art-717d47ef2eb84b978cc44e172e26b939 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-20T07:15:58Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-717d47ef2eb84b978cc44e172e26b9392024-10-01T05:31:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01173e026448110.1371/journal.pone.0264481Hierarchical lifelong topic modeling using rules extracted from network communities.Muhammad Taimoor KhanNouman AzamShehzad KhalidFurqan AzizTopic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and introduce hierarchical lifelong topic models. Hierarchical lifelong topic models not only allow to examine the topics at different levels of granularity but also allows to continuously adjust the granularity of the topics as more information becomes available. A fundamental issue in hierarchical lifelong topic modeling is the extraction of rules that are used to preserve the hierarchical structural information among the rules and will continuously update based on new information. To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. Experimental results indicate improvement of the hierarchical topic structures in terms of topic coherence that increases from general to specific topics.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0264481&type=printable |
| spellingShingle | Muhammad Taimoor Khan Nouman Azam Shehzad Khalid Furqan Aziz Hierarchical lifelong topic modeling using rules extracted from network communities. PLoS ONE |
| title | Hierarchical lifelong topic modeling using rules extracted from network communities. |
| title_full | Hierarchical lifelong topic modeling using rules extracted from network communities. |
| title_fullStr | Hierarchical lifelong topic modeling using rules extracted from network communities. |
| title_full_unstemmed | Hierarchical lifelong topic modeling using rules extracted from network communities. |
| title_short | Hierarchical lifelong topic modeling using rules extracted from network communities. |
| title_sort | hierarchical lifelong topic modeling using rules extracted from network communities |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0264481&type=printable |
| work_keys_str_mv | AT muhammadtaimoorkhan hierarchicallifelongtopicmodelingusingrulesextractedfromnetworkcommunities AT noumanazam hierarchicallifelongtopicmodelingusingrulesextractedfromnetworkcommunities AT shehzadkhalid hierarchicallifelongtopicmodelingusingrulesextractedfromnetworkcommunities AT furqanaziz hierarchicallifelongtopicmodelingusingrulesextractedfromnetworkcommunities |