Abstract
Automatic assignment of documents into some of the categories is highly required for analysing the content of the documents. Thus, document classification plays a significant role in the field of machine learning, artificial intelligence, information extraction, natural language processing and many more. This problem of assigning a document to a particular category or class has been approached in several ways till date, and with numerous new technological advancements, this class of problem has interesting solutions. Apart from the processes related to text analysis and parsing methodologies, deep learning has offered a way to solve this classification scenario. In the present work, we represent a comparative study on some of the basic building blocks used in deep learning, each of which can be applied to get simpler models trying to assign a class of the available documents. The present comparative study shows how these components can vary the impact on the task. The evaluation of the models has been performed on a standard available dataset. The non-linearity provided by these deep learning models are useful in getting state-of-the-art results for text classification problem.
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Manna, M., Das, P., Das, A.K. (2019). Application of Deep Learning Techniques on Document Classification. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_15
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