Machine learning and natural language processing in domain classification of scientific knowledge objects: a review

The domain classification of scientific knowledge objects has been continuously improved over the years. Systems that can automatically classify a scientific knowledge object, through the use of artificial intelligence, machine learning algorithms, natural language processing, and others, have been adopted in most scientific knowledge databases to maintain internal classification consistency as well as to simplify the information arrangement. However, the amount of available data has grown exponentially in the last few years and now it can be found in multiple platforms under different classifications due to the implementation of different classification systems. Thus, the process of searching and selecting relevant data in research studies and projects has become more complex and the time needed to find the right information has continuously grown as well. Therefore, machine learning and natural language processing play an important role in the development and achievement of automatic and standardized classification systems that will aid researchers in their research work.This work has been supported by IViSSEM: POCI-01-0145-FEDER-28284

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user