AppTechMiner: Mining Applications and Techniques from Scientific Articles
This provides a tool for researchers to analyze trends in computational linguistics, but it is incremental as it applies a rule-based framework to a new domain.
The paper tackles the problem of automatically extracting and categorizing application areas and problem-solving techniques from scientific articles, achieving high precision (~82%) and recall (~84%) in knowledge base creation and ~66% accuracy in article categorization.
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.