We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and relation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all possible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we

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... e all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60% in terms of F-score as third rank system among the participated systems. In relation extraction task, we achieved 80.46% in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based information extraction (DyGIE) systems.