Within this paper, we glance at how Named Entity Recognition (NER) may be exploited to slim the gap concerning EL devices trained on significant and reduced quantities of labeled facts. More especially, we present how and to what extent an EL process can take pleasure in NER to reinforce its entity representations, boost candidate choice, decide on simpler unfavorable samples and enforce hard and smooth constraints on its output entities. We release our software – code and design checkpoints – at .
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