Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
This work addresses the challenge of extracting accurate attribute values from noisy web data for emerging entities, which is an incremental improvement in information extraction.
The paper tackles the problem of open attribute value extraction for emerging entities by proposing a knowledge-guided reinforcement learning framework that uses knowledge graphs to filter noisy articles and improve answer accuracy, achieving performance gains of 16.5-27.8% over baselines.
Open attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a \textit{question-answering} (QA) task. While the collections of articles from web corpus provide updated information about the emerging entities, the retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers. Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy. Knowledge graph (KG), which contains rich, well organized information about entities, provides a good resource to address the challenge. In this work, we propose a knowledge-guided reinforcement learning (RL) framework for open attribute value extraction. Informed by relevant knowledge in KG, we trained a deep Q-network to sequentially compare extracted answers to improve extraction accuracy. The proposed framework is applicable to different information extraction system. Our experimental results show that our method outperforms the baselines by 16.5 - 27.8\%.