NMF-based approach to automatic term extraction

作者：

Highlights：

• 5 Different NMF algorithms with different parameters are compared.

• Kullback-Leibler NMF requiring the stationarity of objective value is the best.

• The performance of NMF algorithms depends on a corpus imbalance.

• NMF outperforms 4 from 6 baseline methods and second only to deep learning methods.

摘要

•5 Different NMF algorithms with different parameters are compared.•Kullback-Leibler NMF requiring the stationarity of objective value is the best.•The performance of NMF algorithms depends on a corpus imbalance.•NMF outperforms 4 from 6 baseline methods and second only to deep learning methods.

论文关键词：Automatic term extraction,Probabilistic topic modeling,NMF,Unsupervised term extraction,ACTER dataset,TermEval shared task

论文评审过程：Received 3 June 2021, Revised 13 January 2022, Accepted 1 April 2022, Available online 6 April 2022, Version of Record 9 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117179