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This thesis examines the effectiveness of the latest Transfer Learning techniques for Natural Language Processing applied to the classification of research methods used in scientific journals in the domain of Information Systems. The task of automated knowledge extraction from academic articles has seen ongoing progress in recent years. However, the combination of transfer and Deep Learning in order to assign research methods to scientific papers has not been addressed in the literature yet. The main contribution of this thesis is, therefore, an artifact that applies cutting-edge Transfer Learning techniques to a Deep Learning model by conducting several experiments and comparing their effectiveness. The prototype considers various ways of fine-tuning that are crucial to retain the knowledge transferred from pretrained models and avoid catastrophic forgetting. Additionally, this work discusses the literature with regard to the task-specific Theory Ontology Learning and the method-specific state of the art in Transfer Learning for Natural Language Processing. As a result, the artifact surpassed the performance of previously developed models for research method extraction, presented in the literature, without applying any custom feature engineering and only using around a thousand of labeled observations.
The goal of this research is to investigate the use of deep convolutional neural networks for racing bib number recognition in sport images. Several deep neural network architectures are studied. Three final architectures are trained on three different sets of data: 1) Street View House Numbers (SVHN) Dataset, 2) A private dataset from Flashframe.io from different running events, and 3) A combination of dataset 1 and 2. This thesis investigates the performance that can be obtained on racing bib numbers from a neural network that has been trained on solely images from street house numbers, on a mixture of SVHN and RBN images as well as only on RBN images. The motivation behind this is to see how well this problem can be solved by transfer learning, as labelled images of racing bib numbers are scarce.
The models are tested on the RBNR Dataset (Ben-Ami et al., 2012) and a subset of the private dataset from Flashframe.io. The study shows that the best recognition results were obtained by a model trained on the hybrid dataset of all SVHN images plus an additional 50.000 images. This model outperformed the models that had been trained solely on the SVHN Dataset or the private racing bib number dataset.
The best model resulted in Recall of 0,92, Precision of 0,93 and F-measure of 0,93 on the RBNR Dataset (using the same formulas as previously reported on the RBNR dataset), and 0,97, 0,97 and 0,97 on the private dataset, respectively. The reported recognition results on the RBNR dataset are much higher than previously used methods and proves that neural networks can effectively be used for racing bib number recognition.