When new researchers read scholarly papers, they often encounter unfamiliar technical terms, which may require considerable time to investigate. We have been developing a user interface to support the browsing of scholarly papers, which can provide useful links to information about such technical terms. The interface displays "important terms" extracted from a paper on top of the image of the paper. In this study, we organize the important terms extracted from papers by using author keywords. We first identify the important terms and then associate them with author keywords by using a method based on the word2vec model. Experiments showed that our method improved the classification accuracy of important terms compared with a simple baseline method. It associated each author keyword with about 2.5 relevant important terms.