Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Extracting entities and relations from unstructured text is crucial for knowledge graph construction and information retrieval.
• Existing methods often struggle with simultaneous extraction, leading to suboptimal performance.
• Deep learning and reinforcement learning offer potential for improving information extraction tasks.

Purpose of the Study:

• To develop a novel method for simultaneous entity and relation extraction using reinforcement learning and deep learning.
• To improve the performance of information extraction from unstructured texts.
• To enable feedback between entity and relation extraction stages.

Main Methods:

• A two-step decision process modeled using reinforcement learning.
• Deep learning, specifically bidirectional LSTM and attention mechanisms, to capture contextual information and represent states.
• Tree-LSTM for representing relation mentions and generating transition states.
• Q-Learning algorithm to derive the control policy for the decision process.

Main Results:

• The proposed method successfully extracts entities and relations simultaneously.
• Experimental results on the ACE2005 dataset show superior performance compared to state-of-the-art methods.
• A significant 2.4% increase in recall score was achieved.

Conclusions:

• The integration of reinforcement learning and deep learning provides an effective framework for simultaneous entity and relation extraction.
• The designed reward function facilitates information flow and feedback between extraction components.
• This approach represents a significant advancement in the field of information extraction from text.