A joint triple extraction method by entity role attribute recognition.

Area of Science:

• Natural Language Processing
• Information Extraction
• Artificial Intelligence

Background:

• Joint triple extraction is crucial for information extraction but struggles with complex language, specifically relation overlap.
• Existing models often use complex shared features, leading to redundancy and poor interpretability in predictions.
• These challenges limit the effectiveness of current joint extraction models.

Purpose of the Study:

• To propose a novel model for entity role attribute recognition using triple holistic fusion features.
• To address the challenge of relation overlap in joint triple extraction.
• To develop a model with a simpler and more interpretable prediction process.

Main Methods:

• A new model employing low-level feature separation and high-level concept fusion is introduced.
• Parallel prediction of entities and relationships using low-level token features.
• Feature fusion of candidate triples via residual connections and attention mechanisms, followed by entity role attribute identification.

Main Results:

• The proposed model demonstrates high effectiveness in extracting triples, including overlapping ones.
• Achieves state-of-the-art performance on public datasets for joint triple extraction.
• The prediction process is significantly simpler and more interpretable than existing methods.

Conclusions:

• The novel triple holistic fusion feature model successfully addresses relation overlap in joint triple extraction.
• The model offers improved interpretability and efficiency in information extraction.
• This approach advances the field of natural language processing by providing a more robust extraction method.