In this paper, we propose a deep similarity network-based model combined with the MCTS algorithm to bootstrap Entity Set Expansion.
we leverage the Monte Carlo Tree Search (MCTS) algorithm to efficiently estimate the delayed feedback of each pattern in the bootstrapping; we propose a Pattern Mover Similarity Network (PMSN) to uniformly embed entities and patterns using a distribution on context pattern embeddings; we combine the MCTS and the PMSN to adaptively learn a better embedding for evaluating both patterns and entities.