G
Goran Nenadic
Researcher at University of Manchester
Publications - 197
Citations - 4207
Goran Nenadic is an academic researcher from University of Manchester. The author has contributed to research in topics: Information extraction & Computer science. The author has an hindex of 28, co-authored 175 publications receiving 3266 citations. Previous affiliations of Goran Nenadic include European Bioinformatics Institute & University of Salford.
Papers
More filters
Journal ArticleDOI
Machine learning methods for wind turbine condition monitoring: A review
Adrian Stetco,Fateme Dinmohammadi,Xingyu Zhao,Valentin Robu,David Flynn,Mike Barnes,John A. Keane,Goran Nenadic +7 more
TL;DR: This paper reviews the recent literature on machine learning models that have been used for condition monitoring in wind turbines and shows that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression.
Journal ArticleDOI
Term identification in the biomedical literature
TL;DR: The process of identifying terms is analysed through three steps: term recognition, term classification, and term mapping, and main approaches and general trends, along with the major problems, are discussed.
Journal ArticleDOI
LINNAEUS: A species name identification system for biomedical literature
TL;DR: LINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can be integrated into a range of bioinformatics and text-mining applications.
Journal ArticleDOI
Text mining of cancer-related information: review of current status and future directions
TL;DR: A critical overview of the current state of the art for TM related to cancer is provided and a strong bias towards symbolic methods is highlighted, e.g. named entity recognition based on dictionary lookup and information extraction relying on pattern matching.
Journal ArticleDOI
Clinical Text Data in Machine Learning: Systematic Review.
Irena Spasic,Goran Nenadic +1 more
TL;DR: The data annotation bottleneck is identified as one of the key obstacles to machine learning approaches in clinical NLP, and future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which do not require data annotation.