[1706.05719] Towards the Improvement of Automated Scientific Document Categorization by Deep Learning
It can be concluded that deep learning based classifiers are a good choice for scientific document categorization and that they can be abstracted well using a generic API.
Abstract: This master thesis describes an algorithm for automated categorization of
scientific documents using deep learning techniques and compares the results to
the results of existing classification algorithms. As an additional goal a
reusable API is to be developed allowing the automation of classification tasks
in existing software. A design will be proposed using a convolutional neural
network as a classifier and integrating this into a REST based API. This is
then used as the basis for an actual proof of concept implementation presented
as well in this thesis. It will be shown that the deep learning classifier
provides very good result in the context of multi-class document categorization
and that it is feasible to integrate such classifiers into a larger ecosystem
using REST based services.
‹Figure 0.1: Attempt at linear separation of samples in two dimensions (Support Vector Machines)Figure 0.2: Plot of the hyperbolic tangent activation function (Activation Functions)Figure 0.3: Plot of the ReLU activation function (Rectified Linear Activation Functions)›