Papers by Guillermo Cecchi

1 papers
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction (D19-1)

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Challenge: Recent studies have shown that locality sensitive hashcodes are useful for biomedical relation extraction tasks.
Approach: They propose to optimize locality sensitive hashcode representations in a nearly unsupervised manner . they use only data points, but not their class labels, for learning .
Outcome: The proposed approach improves accuracy from training to test sets, and the data points are only used for learning .

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