AppTechMiner: Mining Applications and Techniques from Scientific Articles

TL;DR

AppTechMiner is a rule-based system that automatically extracts and categorizes application areas and techniques from scientific articles, achieving high accuracy and enabling insights into research trends.

Contribution

The paper introduces a novel framework for automatic knowledge base construction and article categorization in scientific literature, with high precision and recall.

Findings

Knowledge base creation accuracy ~82% precision and ~84% recall

Article categorization accuracy ~66%

Framework applicable beyond computational linguistics

Abstract

This paper presents AppTechMiner, a rule-based information extraction framework that automatically constructs a knowledge base of all application areas and problem solving techniques. Techniques include tools, methods, datasets or evaluation metrics. We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article. Our system achieves high average precision (~82%) and recall (~84%) in knowledge base creation. It also performs well in application and technique assignment to an individual article (average accuracy ~66%). In the end, we further present two use cases presenting a trivial information retrieval system and an extensive temporal analysis of the usage of techniques and application areas. At present, we demonstrate the framework for the domain of computational linguistics but this can be easily generalized to any other field of research.