Document-level DDI relation extraction with document-entity embedding

Mingliang Dou, Jijun Tang, Fei Guo

Abstract: DDI is an important part of drug-related research and pharmacovigilance. Extracting DDI information from scientific literature has become a low-cost and highly reliable way. Currently, existing works are all sentence-level DDI relation extraction. In fact, the entity relationship is often expressed by multiple sentences. Moreover, the sentence-level DDI relation extraction also causes a large amount of redundancy in the whole dataset with increasing in negative instance data. In this study, we propose a document-level DDI relation extraction method based on document-entity embedding. Our method performs special processing on the DDI Extraction 2013 for the first time, in order to calculate document-level relation extraction. For obtaining document-level entity information, we propose a document-entity embedding method to integrate the information of all same drugs in the same article. The experimental results show that the processing of DDI Extraction 2013 dataset is reasonable. In addition, the proposed method has achieved good performance on document-level DDI dataset, and the best F1 score is 62.51%. This is the first time that DDI Extraction 2013 has been processed into a document-level dataset, and document-level DDI relation extraction has been realized.