Skill Translation Models in Expert Finding
Arash Nobari, Sajad Sotudeh Gharebagh, Mahmood Neshati · 2017
Finding talented users on Stackoverflow can be a challenging task due to term mismatch between queries and content published on it. In this paper, we propose two translation models to augment a given query with relevant words. The first model is based on a statistical approach and the second one is a word embedding model. Interestingly, the translations provided by these methods are not the same. Although the first model in most cases selects pieces of program codes as translations, the second model provides more semantically related words. Our experiments on a large dataset indicate the efficiency of proposed models.