Аннотация:In this paper we describe rule-based and neural approaches to gapping resolution task for Russian language. Our study was conducted on the material of AGRR-2019 Shared Task. We demonstrate that neural model definitively outperforms the rule-based one even when only 2000 annotated sentences are available. The rule-based model took the 6th place in AGRR-2019 competition (2nd in terms of precision), while the neural one was better than the second-ranked system.