An Analysis of Relation Extraction within Sentences from Wet Lab Protocols

Wet lab protocols (WLPs) are sets of instructions written in domain-specific natural language for step-by-step biological experimental processes. There have been efforts to annotate WLPs for shallow semantic parsing to enable reproducible procedures, text mining, and automatic conversion into a machine-readable format. However, current methods have not fully exploited the relation extraction sub-task on the protocol corpus. Neural approaches have the potential to deal with the various noise and in-domain jargon in the texts. To explore the viability of neural methods for this task, we perform a thorough analysis of both graph and nongraph neural approaches. We find that both graph neural networks with generated parameters (GP-GNNs) and Context-Aware models show advantages in relation extraction and are well suited to our goal. Specifically, the GP-GNNs and Context-Aware models demonstrate similar performance on all three WLPs datasets when the full training set is used, both outperforming the previous best results significantly. This can be explained by the observation that considering multiple relations in a sentence enhances the predictive ability. In addition, our extensive experiments demonstrate that the Context-Aware approach in particular can achieve good results even with a limited amount of training data, providing new insights for low-resource scenarios.