Since it is infeasible to estimate the quality of all possible taxonomies, we design an end-to-end reinforcement learning (RL) model to combine the two phases.
we train an RL agent that employs the term pair representations using multiple sources of information and determines which term to select and where to place it on the taxonomy via a policy network.