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.
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Hariharan, V., Anand Kumar, M., Soman, K.P. (2019). Relation Extraction Using Convolutional Neural Networks. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_90
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DOI: https://doi.org/10.1007/978-3-030-00665-5_90
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