Relation classification via BERT with piecewise convolution and focal loss.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Existing relation extraction models evolved from shallow networks to advanced models like BERT.
• These models often overlook crucial semantic information within sequences and struggle with distance dependence.
• Internal semantic information holds potential for enhancing relation classification accuracy.

Purpose of the Study:

• To propose a novel BERT-based relation classification method.
• To effectively capture internal semantic information between entity pairs.
• To address and improve upon the distance dependence problem in relation extraction.

Main Methods:

• Utilized a pre-trained BERT model for fine-tuning to extract semantic representations.
• Employed piecewise convolution to capture influential semantic information within sequences.
• Incorporated the focal loss function to handle class imbalance and improve generalization.

Main Results:

• The proposed BERT-based model demonstrated superior performance in relation extraction.
• Achieved a significant F1 score of 89.95% on the SemEval-2010 Task 8 dataset.
• Effectively extracted internal semantic information, enhancing classification accuracy.

Conclusions:

• The proposed method offers a significant advancement in relation extraction tasks.
• Capturing internal semantic information and addressing distance dependence are key to improved accuracy.
• The focal loss function aids in generalizing to datasets with varying relationship category distributions.