A Comparison of Different Approaches to Document Representation in Turkish Language

Recently, deep learning methods have demonstrated state-of-the-art performance in numerous complex Natural Language Processing (NLP) problems. Easy accessibility of high-performance computing resources and open-source libraries makes Artificial Intelligence (AI) approaches more applicable for researchers. This sudden growth of available techniques shaped and improved standards in the field of NLP. Thus, we find an opportunity to compare different approaches to document representation, owing to various open-source libraries and a large amount of research. We evaluate four different paradigms to represent documents: Traditional bag-of-words approaches, topic modeling, embedding based approach and deep learning. As the main contribution of this article, we aim at evaluating all these representation approaches with suitable machine learning algorithms for document categorization problem in the Turkish language. The supervised architecture uses a benchmark dataset specifically prepared for this language. Within the architecture, we evaluate the representation approaches with corresponding machine learning algorithms such as Support Vector Machine (SVM), multi-nominal Naive Bayes Algorithm (m-NB) and so forth. We conduct a variety of experiments and present successful results for the Turkish document categorization. We also observed that tradition approaches have still comparable results with Neural Network models in terms of document classification.