End-to-End Reinforcement Learning for Automatic Taxonomy Induction

ACL 2018 Yuning MaoXiang RenJiaming ShenXiaotao GuJiawei Han

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy... (read more)

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