Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning

Understand

In this paper we address a task of relation mention extraction from noisy data: extracting representative phrases for a particular relation from noisy sentences that are collected via distant supervision.

• Despite its significance and value in many downstream applications, this task is less studied on noisy data.
• The major challenges exists in 1) the lack of annotation on mention phrases, and more severely, 2) handling noisy sentences which do not express a relation at all.
• To address the two challenges, we formulate the task as a semi-Markov decision process and propose a novel hierarchical reinforcement learning model.