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This paper describes the approach presented by the LSI_UNED team in the Multilingual Information Extraction task (SpRadIE) of CLEF eHealth 2021. The proposed system is a deep learning stack designed for separately detecting negation hedge cues and other biomedical entities in the task. Transfer learning techniques are applied for studying whether pre-trained weights from a different negation detection task can be effectively incorporated into the model for improving a baseline system traineddblp:conf/clef/FabregatDAM21 fatcat:6pdhmdq6brezbkaoyp2oxlzavy