Multiple Relations Classification using Imbalanced Predictions Adaptation
This work addresses a specific issue in relation classification for text mining applications like knowledge graph construction, but it is incremental as it builds on existing models by focusing on imbalanced predictions.
The paper tackled the problem of imbalanced predictions in multiple relation classification, where few valid relations exist among many predefined ones, and proposed a model with a customized output architecture and additional input features that achieved significant improvements on benchmark datasets.
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.