Towards the Improvement of Automated Scientific Document Categorization by Deep Learning

2017/06/18 by Thomas Krause, Krause, Thomas
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Text and Document Classification Technologies #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1706.05719

openalex publication_date 2017/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Efficient Estimation of Word Representations in Vector Space
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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