Distant supervision for neural relation extraction integrated with word attention and property features

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Distant supervision is an efficient method for large-scale relation extraction from text.
• Current neural methods struggle to identify critical words during sentence encoding and may miss important contextual information for positive training instances.

Purpose of the Study:

• To propose a novel neural relation extraction model that addresses limitations in existing methods.
• To enhance the capture of critical words and incorporate supplementary sentence information for improved relation extraction.

Main Methods:

• Development of a word-level attention mechanism to assign importance weights to individual words within a sentence.
• Investigation of semantic information from target entity word embeddings as a supplementary feature for the relation extraction model.

Main Results:

• The proposed model demonstrates superior performance compared to existing state-of-the-art baselines in neural relation extraction.
• The word-level attention mechanism effectively highlights critical words, improving sentence encoding.
• Utilizing semantic information from entity embeddings provides valuable supplementary features.

Conclusions:

• The novel neural relation extraction model significantly improves upon existing methods.
• The integration of word-level attention and entity semantic information enhances the accuracy and efficiency of distant supervision for relation extraction.