What is the real novelty of a research paper? How do different researchers contribute to innovation? And does this change throughout their career? Shubhanshu Mishra of the University of Illionois uses textmining techniques to study the novelty of biomedical articles.
Systematic review of medical research papers can lead to new knowledge and treatments of diseases. The existing software tools however, are very limited and often a lot of manual work is involved. Stephen Gilbert of Iowa State University uses artificial intelligence and machine learning to automate the process of systematic review.
While discussions at the EU on copyright reform and an exception for text and data mining (TDM) are very much live, FutureTDM, a Commission funded project of TDM experts has, for the past year, already been gathering information, mapping the TDM landscape and listening to the wide variety of individuals and organisations involved in data analytics. The project has just produced the first in a series of reports, providing a range of stakeholders with recommendations to improve TDM uptake in the EU. This FutureTDM policy framework document sets out high-level principles and recommendations.
Frederico Nanni was not always a text miner. He actually started out as a historian and then switched to digital humanities. During his PhD, he developed a method to detect interdisciplinary research, based on scientific abstracts. Now, he finds text mining fascinating and thinks more historians should learn how to do it.
It took some time for Drahomira Hermannova to see the value of her research topic, but now she thinks it is the best topic she could ever choose: using text and data mining to evaluate which research can change the world. Not only can this help scientists, it may change the way research is done altogether.
In the OpenMinTeD project, partners from different scientific communities are involved to make sure the OpenMinTeD infrastructure will address their needs. As regards the social sciences, a useful application for text mining is the improvement of literature search and information interlinking. To this end, three main challenges were identified: named entity recognition, automatic keyword assignment to texts and automatic detection of mentions of survey variables. This post gives an overview of these tasks and the progress of work so far.