Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels
作者：
Highlights：
• We propose a machine learning-based technique for activity label analysis.
• We conceptualize activity label analysis as a tagging task based on a Hidden Markov Model.
• Our technique overcomes the issues associated with the rule-based state of the art.
• Our technique no longer requires the manual specification of rules.
• A comparative evaluation with 15,000 labels demonstrates the superiority of our technique.
摘要
•We propose a machine learning-based technique for activity label analysis.•We conceptualize activity label analysis as a tagging task based on a Hidden Markov Model.•Our technique overcomes the issues associated with the rule-based state of the art.•Our technique no longer requires the manual specification of rules.•A comparative evaluation with 15,000 labels demonstrates the superiority of our technique.
论文关键词：Label analysis,Process model,Natural language,Hidden Markov models
论文评审过程：Received 27 December 2016, Revised 19 October 2018, Accepted 13 February 2019, Available online 17 February 2019, Version of Record 22 February 2019.
论文官网地址：https://doi.org/10.1016/j.is.2019.02.005
