Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction

IJCNLP 2019 Sahil GargAram GalstyanGreg Ver SteegGuillermo Cecchi

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... (read more)

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