Exploring developments of the AI field from the perspective of methods, datasets, and metrics

The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets, and metrics, are extracted and used as artificial intelligence (AI) markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the named entity recognition model is used to extract AI markers from large-scale AI literature. A multi-stage self-paced learning strategy (MSPL) is proposed to address the negative influence of hard and noisy samples on the model training. Secondly, original papers are traced for AI markers. Statistical and propagation analyses are performed based on the tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters is explored. The above-mentioned mining based on AI markers yields many significant findings. For example, the propagation rate of the datasets gradually increases. The methods proposed by China in recent years have an increasing influence on other countries.

Automated summary

This study proposes a method to trace and mine valuable information in academic literature using artificial intelligence (AI) markers. Firstly, AI markers are extracted from large-scale AI literature using a named entity recognition model. Then, original papers are traced for AI markers and statistical and propagation analyses are performed. Finally, clustering analysis is conducted using co-occurrences of AI markers to explore the evolution within method clusters. The study reveals significant findings, such as the increasing propagation rate of datasets and the growing influence of methods proposed by China in recent years on other countries.