Speaker
Description
Researchers and students are overwhelmed by the current rate of scientific paper publishing. As a consequence, their literature search and review activity has become more complex and time-consuming. The Information needs vary widely in different academic disciplines. The traditional search technology requires keywords, however, keywords are a limited representation of information needs. The future scientific knowledge management system ought to be a dialogue between the user and the system, requiring integration of language, scientific domain knowledge, and understanding of user information need. Instead of requiring of the user to convert the information need into a set of English keywords, the system should aid the user to represent the information need on a conceptual level by means of user contexts. The text mining research must go towards categorizing the information needs and sub discipline of scientific fields. Then deploy the optimal models or techniques for each particular category of information need. I argue that, by integrating an eLearning system and combining the benefits of supervised learning with reinforcement learning, we obtain partly self-learned annotated data that will boost the efficiency of customized solutions for each specific information need, and, at the same time, will provide users with added knowledge values.
