# AppTechMiner: Mining Applications and Techniques from Scientific Articles

arXiv: https://arxiv.org/abs/1709.03064 · HTML: https://mlsanity.com/paper/1709.03064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles](https://mlsanity.com/paper/2109.00199/) (2021)

## Abstract (arXiv)

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.