To address the above two challenges, we propose a novel bootstrapping method combining the Monte Carlo Tree Search (MCTS) algorithm with a deep similarity network, aiming to effectively evaluate the delayed feedback of patterns and adaptively score entities given sparse supervision signals.
our method tackles the delayed feedback problem by enhancing the traditional bootstrapping method using the MCTS algorithm, which effectively estimates each pattern's delayed feedback via efficient multi-step lookahead search.