Three-way multi-granularity learning towards open topic classification

Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incremen-tally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular comput-ing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of pro-posed models and the efficiency of knowledge accumulation with classic models. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a unified framework of three-way multi-granularity learning for open topic classification. It uses multilevel granular structure of tasks, adaptive decision boundary, and three-way enhanced clustering to detect and explore unknown topics in open dynamic environments. The approach also incorporates knowledge accumulation and compares the performance with classic models through experiments.