Relation Extraction (RE) is the task of finding the relation between entities in a plain text. As the length of the sentences increases, finding the relation becomes more challenging. The shortest dependency path (SDP) between two entities, obtained by traversing the terms in the dependency tree of a sentence, provides a view focused on the entities by pruning noisy words. In the supervised form of the relation extraction task, Relation Classification, the state-of-the-art methods generally integrate a pre-trained language model (PLM) into their approaches. However, none of them incorporates the shortest dependency paths to the best of our knowledge.This paper investigates the effects of using shortest dependency paths with pre-trained language models by taking the R-BERT relation classification model as the baseline and building upon it. Our novel approach enhances the baseline model by adding the sequence representation of the shortest dependency path between entities, collected from PLMs, as an additional embedding. In the experiments, we evaluated the proposed model’s performance for each combination of SDPs generated from Stanford, HPSG, and LAL dependency parsers with BERT and XLNet PLMs in two datasets, SemEval-2010 Task 8 and TACRED. We improved the baseline model by absolute 1.41% and 3.60% scores, increasing the rankings of the model from 8^th to 7^th and from 18^th to 7^th in SemEval-2010 Task 8 and TACRED, respectively.