Introduction Welcome to the First Aha!-workshop on Information Discovery in Text!

Recent approaches to relation extraction following the distant supervision paradigm have focused on exploiting large knowledge bases, from which they extract substantial amount of supervision. However, for many relations in real-world applications, there are few instances available to seed the relation extraction process, and appropriate named entity recognizers which are necessary for pre-processing do not exist. To overcome this issue, we learn entity filters jointly with relation extraction using imitation learning. We evaluate our approach on architect names and building completion years, using only around 30 seed instances for each relation and show that the jointly learned entity filters improved the performance by 30 and 7 points in average precision.