The relation classification task assigns the proper semantic relation to a
pair of subject and object entities; the task plays a crucial role in various
text mining applications, such as knowledge graph construction and entities
interaction discovery in biomedical text. Current relation classification
models employ additional procedures to identify multiple relations in a single
sentence. Furthermore, they overlook the imbalanced predictions pattern. The
pattern arises from the presence of a few valid relations that need positive
labeling in a relatively large predefined relations set. We propose a multiple
relations classification model that tackles these issues through a customized
output architecture and by exploiting additional input features. Our findings
suggest that handling the imbalanced predictions leads to significant
improvements, even on a modest training design. The results demonstrate
superiority performance on benchmark datasets commonly used in relation
classification. To the best of our knowledge, this work is the first that
recognizes the imbalanced predictions within the relation classification task.

通过定制的输出架构和利用额外的输入特征，我们提出了一个多关系分类模型，用于解决关系分类任务中的不平衡预测问题，并在提供的基准数据集上展示了其卓越性能，从而证明了处理不平衡预测对结果的显著改进。