A neuralized feature engineering method for entity relation extraction.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Entity relation extraction is crucial for understanding text, but current neural networks struggle with encoding semantic and structural information for relation instances.
• Existing feature-based models leverage prior knowledge but lack the automatic feature learning of neural networks.

Purpose of the Study:

• To propose a novel neuralized feature engineering approach for enhancing entity relation extraction.
• To improve the encoding of semantic and structural information within neural networks for relation extraction tasks.

Main Methods:

• Developed a neuralized feature engineering approach that integrates manually designed features into neural networks.
• Encoded expert-designed features into distributed representations to enhance neural network discriminability.
• Utilized stacked neural layers for automatic high-order feature extraction from raw inputs.

Main Results:

• The proposed approach significantly improved entity relation extraction performance compared to standalone neural networks or feature-based models.
• Achieved over 8% and 16.5% higher F1-scores on the ACE and Chinese literature text corpora, respectively.
• Demonstrated the effectiveness of combining prior knowledge with deep learning for relation extraction.

Conclusions:

• Neuralized feature engineering offers a powerful hybrid approach for entity relation extraction.
• Integrating manually designed features enhances neural network capabilities, leading to state-of-the-art results.
• This method effectively leverages both explicit knowledge and implicit feature learning for improved text understanding.