The joint extraction of fact-condition statement and super relation in scientific text with table filling method

Abstract

The fact-condition statements are of great significance in scientific text, via which the natural phenomenon and its precondition are detailly recorded. In previous study, the extraction of fact-condition statement and their relation (super relation) from scientific text is designed as a pipeline that the fact-condition statement and super relation are extracted successively, which leads to the error propagation and lowers the accuracy. To solve this problem, the table filling method is firstly adopted for joint extraction of fact-condition statement and super relation, and the Biaffine Convolution Neural Network model (BCNN) is proposed to complete the task. In the BCNN, the pretrained language model and Biaffine Neural Network work as the encoder, while the Convolution Neural Network is added into the model as the decoder that enhances the local semantic information. Benefiting from the local semantic enhancement, the BCNN achieves the best F1 score with different pretrained language models in comparison with other baselines. Its F1 scores in GeothCF (geological text) reach 73.17% and 71.04% with BERT and SciBERT as pretrained language model, respectively. Moreover, the local semantic enhancement also increases its training efficiency, via which the tags’ distribution can be more easily learned by the model. Besides, the BCNN trained with GeothCF also exhibits the best performance in BioCF (biomedical text), which indicates that it can be widely applied for the information extraction in all scientific domains. Finally, the geological fact-condition knowledge graph is built with BCNN, showing a new pipeline for construction of scientific fact-condition knowledge graph.