Text/Conference Paper

In recent years, there has been an increasing interest in the task of relation classiĄcation, which aims to label a relation between two semantic entities. In this work, we investigate how domain-speciĄc information inĆuences the performance of ClaiRE, an SVM-based system combining manually crafted features with word embeddings. To this end, we experiment with a wide range of word embeddings and evaluate on one general and two scientiĄc relation classiĄcation datasets. We release all of our code for relation classiĄcation and data for scientiĄc word embeddings to enable the reproduction of our experiments.2