Abstract

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.

Highlights

  • Lifelong topic models enable the conventional topic model to learn topics continuously from the knowledge accumulated from the past which is updated regularly based on new information

  • Hierarchical topic modeling extends topic modeling for grouping into a hierarchical structure

  • This study combines the two and proposes hierarchical lifelong topic models which allows the examination of topics at different levels of granularity and continuously adjust the granularity of the topics as more information is made available

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Summary

Introduction

Topic models process a collection of documents to extract hidden thematic structures called topics [1, 2]. Whenever new information is available, the same support is required To overcome these issues, we present an approach called network communities based rule mining for hierarchical lifelong topic models or NHLTM. The existing lifelong topic modeling approaches are not well suited for finding the associations among topics [51, 52] They lack in conveying structural information which is a key constituent for extracting topic hierarchies [52, 53]. Different approaches are available for finding the association among rules that could be translated into improving the arrangement of topics within a dataset and that of words within a topic. The proposed community detection approach for mining rules facilitate in improving coherence of topics and arrange them into a hierarchical structure. We elaborate the community detection process for rule mining employed in the algorithm in some detail

14: Represented the communities as rules
Experimental results and discussion
Conclusion
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