AI for AI: Using AI methods for classifying AI science documents

Abstract

Subject area classification is an important first phase in the entire process involved in bibliometrics. In this paper, we explore the possibility of using automated algorithms for classifying scientific papers related to Artificial Intelligence at the document level. The current process is semi-manual and journal-based, a realisation that, we argue, opens up the potential for inaccuracies. To counter this, our proposed automated approach makes use of neural networks, specifically BERT. The classification accuracy of our model reaches 96.5%. In addition, the model was used for further classifying documents from 26 different subject areas from the Scopus database. Findings indicate that a significant subset of existing Computer Science, Decision Science and Mathematics publications could potentially be classified as AI-related. The same holds in particular cases in other science fields such as Medicine and Psychology or Arts and Humanities. The above indicate that in subject area classification processes, there is room for automatic approaches to be utilised in a complementary manner with traditional manual procedures. Peer Review https://publons.com/publon/10.1162/qss_a_00223

A Comprehensive Overview of Artificial Intelligence-Based Classification Techniques

“…The objective of this paper is to provide a comprehensive understanding of classification techniques in AI and their importance in enabling machines to learn from data and conclude accurate predictions or decisions. [1] [2] The goal of the computer science discipline of Artificial intelligence (AI) is to build the intelligent machines that are capable of the activities normally require human intelligence, such as visual perception, speech recognition, decisionmaking, and natural language processing. AI can be classified into different types based on its techniques and applications.…”