Large Scientific repositories can be accessed through the current technologies by various means such as simple keyword searches for content and authors which automatically identify sections and section labels, and unsupervised methods to infer information structures. Unfortunately, these access methods fall short of supporting many queries that could significantly improve the day-to-day activities of a researcher. For example, a researcher who wants to keep track of recent developments in the field of natural language processing ideally would like to quickly get answers to questions like • what algorithm have better results? • What are the papers that work on a larger training set? Answering such queries is beyond the state of the art.
Using RPA methodologies, the main elements of the experiments and the relationship between the elements cannot be inferred from the repository. The paper discusses on recent developments for automatic extraction powered by cognitive technology that enables automatic images and documents extraction, classification, correction and storage for eﬀectively identifying the named entities and relations. This enables effective information management of unstructured content in Scholarly and Journals Documents. Download whitepaper to know more.