AI Marker-based Large-scale AI Literature Mining

TL;DR

This paper introduces a novel AI literature mining approach using AI markers like methods, datasets, and metrics to trace research evolution, analyze influence, and cluster research scenes in large-scale AI publications.

Contribution

It proposes an entity extraction model for AI markers, enabling detailed tracing, analysis, and clustering of AI research literature, revealing research trends and influence patterns.

Findings

Rapid increase in propagation of effective methods over time

China's recent methods have growing influence internationally

Saliency detection remains relatively unaffected by other research scenes

Abstract

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 used as 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 entity extraction model is used in this study to extract AI markers from large-scale AI literature. Secondly, original papers are traced for AI markers. Statistical and propagation analysis are performed based on tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters and the influencing relationships amongst different research scene clusters are explored. The above-mentioned mining based on AI markers yields many meaningful discoveries. For example, the propagation of effective methods on the datasets is rapidly increasing with the development of time; effective methods proposed by China in recent years have increasing influence on other countries, whilst France is the opposite. Saliency detection, a classic computer vision research scene, is the least likely to be affected by other research scenes.