A discrete convolutional network for entity relation extraction.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Relation extraction is crucial for understanding text but faces challenges due to complex semantic structures.
• Current methods often rely on manually designed rules or complex deep learning architectures.
• These approaches can lead to issues with overfitting and high computational costs.

Purpose of the Study:

• To propose a novel discrete convolutional network (CNN) for relation extraction.
• To leverage discrete linguistic interactions and deep feature weighting for improved performance.
• To reduce overfitting and computational complexity in relation extraction models.

Main Methods:

• A discrete CNN is introduced, discretizing convolutional kernel parameters into ternary values.
• Discretized kernels are employed to learn discrete semantic structures from token representations.
• The approach captures discrete linguistic patterns within sentences.

Main Results:

• The discrete CNN achieves state-of-the-art performance across five benchmark datasets.
• It outperforms existing relation extraction methods.
• Experimental results show an average F1-score improvement of 14.66% and a 17.46% training acceleration compared to traditional CNNs.

Conclusions:

• The proposed discrete CNN is effective and efficient for relation extraction.
• It successfully captures discrete linguistic patterns, enhancing model expressiveness.
• The method offers advantages in reducing overfitting and computational complexity.