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Relation extraction using convolutional neural networks

Publication Type : Book Chapter

Publisher : Springer Netherlands

Source : Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 30, p.937-944 (2019)

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Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2019

Abstract : Identifying the relationship between the entities plays a key role in understanding any natural language. The relation extraction is a task, which finds the relationship between entities in a sentence. The relation extraction and named entity recognition are the subtasks of information extraction. In this paper, we have experimented and analyzed the closed-domain relation extraction using three variants of temporal convolutional neural network on SemEval-2018 and SemEval-2010 relation extraction corpus. In this approach, the word-level features are formed from the distributed representation of text and the position information of entity are used as the feature for the model. © Springer Nature Switzerland AG 2019.

Cite this Research Publication : V. Hariharan, M. Kumar, A., and Dr. Soman K. P., “Relation extraction using convolutional neural networks”, in Lecture Notes in Computational Vision and Biomechanics, vol. 30, Springer Netherlands, 2019, pp. 937-944.

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