Retrieving research papers and patents is important for any researcher assessing the scope of a field with high industrial relevance. However, the terms used in patents are often more abstract or creative than those used in research papers, because they are intended to widen the scope of claims. Therefore, a method is required for translating scholarly terms into patent terms. In this paper, we propose six methods for translating scholarly terms into patent terms using two synonym extraction methods: a statistical machine translation (SMT)-based method and a distributional similarity (DS)-based method. We conducted experiments to confirm the effectiveness of our method using the dataset of the Patent Mining Task from the NTCIR-7 Workshop. The aim of the task was to classify Japanese language research papers (pairs of titles and abstracts) using the IPC system at the subclass (third level), main group (fourth level), and subgroup (the fifth and most detailed level). The results showed that an SMT-based method (SMT_ABST+IDF) performed best at the subgroup level, whereas a DS-based method (DS+IDF) performed best at the subclass level.