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Causal relation extraction of biomedical entities is one of the most complextasks in biomedical text mining, which involves two kinds of information:entity relations and entity functions. One feasible approach is to takerelation extraction and function detection as two independent sub-tasks.However, this separate learning method ignores the intrinsic correlationbetween them and leads to unsatisfactory performance. In this paper, we proposea joint learning model, which combines entity relation extraction and entityfunction detection to exploit their commonality and capture theirinter-relationship, so as to improve the performance of biomedical causalrelation extraction. Meanwhile, during the model training stage, differentfunction types in the loss function are assigned different weights.Specifically, the penalty coefficient for negative function instances increasesto effectively improve the precision of function detection. Experimentalresults on the BioCreative-V Track 4 corpus show that our joint learning modeloutperforms the separate models in BEL statement extraction, achieving the F1scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations,respectively. This demonstrates that our joint learning system reaches thestate-of-the-art performance in Stage 2 compared with other systems.

Joint Learning-based Causal Relation Extraction from Biomedical Literature