Span-based single-stage joint entity-relation extraction model.

Area of Science:

• Natural Language Processing
• Machine Learning
• Information Extraction

Background:

• Extracting entities and relations from unstructured text is a growing area of research.
• Existing methods struggle with entity overlap and exposure bias, leading to cascading errors.
• There is a need for more robust approaches to handle complex relation extraction tasks.

Purpose of the Study:

• To propose a joint entity relation extraction model (SMHS) that addresses limitations of current methods.
• To transform entity relation extraction into a span-level multi-head selection problem.
• To improve the accuracy and robustness of relation extraction from text.

Main Methods:

• Developed a span-level multi-head selection mechanism for joint entity and relation extraction.
• Utilized span-tagger and span-embedding to create span semantic vectors.
• Employed LSTM and multi-head self-attention for feature extraction, with span classification for multi-task learning.

Main Results:

• The proposed SMHS model demonstrated superior performance compared to baseline methods.
• Experiments on the NYT (English) and DuIE 2.0 (Chinese) datasets confirmed the model's effectiveness.
• The method successfully decodes relation triads in a single stage, mitigating cascading errors.

Conclusions:

• The SMHS model effectively tackles entity overlap and exposure bias in relation extraction.
• The span-level multi-head selection mechanism offers a promising direction for future research.
• The study validates the proposed method's effectiveness on diverse datasets.