Abstract
The performance of keyword expansion in prior methods is often enhanced by adopting external knowledge. Given a set of initial keywords, this paper is motivated to propose a novel method to expand semantically or conceptually related keywords from domain corpus by employing mass diffusion. A bipartite word network is thus constructed based on co-occurrence relations between initial keywords and candidate words. The expanded keywords are identified via two-step mass diffusion which is carried out in the bipartite network. Experimental results prove that the proposed method outperforms both the typical statistical-based approach and graph-based approach. Our research is expected to complement the theoretical framework of keyword expansion and is applicable to the scenarios of query expansion, thesaurus construction, and text clustering.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (71771177, 71601119, 71874088), Innovation Fund for University Production, Education and Research from China’s Ministry of Education (2019J01012), and International Exchange Program for Graduate Students, Tongji University (201902027). The authors thank the editor and the anonymous reviewers for their helpful comments and suggestions in improving this manuscript.
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Yin, X., Wang, H., Yin, P. et al. A co-occurrence based approach of automatic keyword expansion using mass diffusion. Scientometrics 124, 1885–1905 (2020). https://doi.org/10.1007/s11192-020-03601-7
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DOI: https://doi.org/10.1007/s11192-020-03601-7