Evaluating human versus machine learning performance in classifying research abstracts

• Main Authors: GOH, Yeow Chong, CAI, Xin Qing, THESEIRA, Walter, KO, Giovanni, KHOR, Khiam Aik
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Discipline classification; Text classification; Supervised classification; Artificial Intelligence and Robotics; Economics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2446; https://ink.library.smu.edu.sg/context/soe_research/article/3445/viewcontent/Goh2020_Article_EvaluatingHumanVersusMachineLe.pdf
• Institution: Singapore Management University

We study whether humans or machine learning (ML) classification models are better at classifying scientific research abstracts according to a fixed set of discipline groups. We recruit both undergraduate and postgraduate assistants for this task in separate stages, and compare their performance agai...