Enhancing topic-detection in computerized assessments of constructed responses with distributional models of language
作者：
Highlights：
• Inbuilt Rubric is presented here as a “non-Latent” Semantic Analysis approach.
• This method transforms latent semantic space into a non-latent and meaningful one.
• It represents meaning in multi-vector representations for text processing.
• Its performance was significantly higher than the cosine-based similarity.
• It could enhance content-detection in expert and intelligent systems applications.
摘要
•Inbuilt Rubric is presented here as a “non-Latent” Semantic Analysis approach.•This method transforms latent semantic space into a non-latent and meaningful one.•It represents meaning in multi-vector representations for text processing.•Its performance was significantly higher than the cosine-based similarity.•It could enhance content-detection in expert and intelligent systems applications.
论文关键词：Inbuilt rubric,Constructed responses,Summaries,Topic detection,Latent semantic analysis,Automated summary evaluation
论文评审过程：Received 17 July 2020, Revised 2 July 2021, Accepted 13 July 2021, Available online 20 July 2021, Version of Record 26 July 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115621
