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Hierarchical lifelong topic modeling using rules extracted from network communities
2022
PLoS ONE
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
doi:10.1371/journal.pone.0264481
pmid:35239700
pmcid:PMC8893656
fatcat:gpwnrciaoza4powmjrwawfpsa4