Clustered Capsule Network Architecture for Text Classification
Madhuram M1, Mayukh Dasgupta2, Aqib Muhammed Ashik B.T3, Surya M.4
1Madhuram M, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Mayukh Dasgupta, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Aqib Muhammed Ashik B.T, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Surya M., Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 225-227 | Volume-8 Issue-5, June 2019 | Retrieval Number: D6040048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we show that capsule network with some changes in its architecture and with the help of dynamic routing can mimic the speech processing section of the brain to some extent. The results obtained are state of the art and it also challenges some aspects of the capsule network architecture proposed by [Hinton et al., 2017]. This paper also makes a few changes in the selection procedure of the N-gram model proposed by [Wei Zhao et al., 2018]. The paper proposes the idea of mimicking the brain architecture for speech recognition using capsule network by clustering the final capsules into groups of similar lengths of vectors which may represent a specific section of the brain to understand properties of a text. As a result the instantiation properties of text are not lost.
Keywords: Capsule Network, Dynamic Routing, Machine Learning, Natural Language Processing, N-grams Model
Scope of the Article: Natural Language Processing