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Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifier following the prior work. This nearly
doi:10.18653/v1/d19-1414
dblp:conf/emnlp/GargGSC19
fatcat:zhxhvwfbnjh43ep4pdsb54exfi