Year: 2025
Type: conference-paper
Abstract: In order to solve the problems of insufficient sentence feature extraction in traditional models and poor recognition of overlapping entities by previous models, this paper proposes to use the RoBERTa pre-training model with stronger sentence feature extraction capabilities and the pointer annotation network with stronger overlapping entity recognition capabilities for entity recognition. Relation extraction. This method uses the RoBERTa pre-training model to extract richer sentence features, and uses pointer annotation to mark the starting position and ending position of entities on the same two sequences, thereby effectively extracting overlapping entities. Through experimental verification, the model in this paper has better performance improvement on the DuIE benchmark data set.
Authors: Xin Zhang, Baolin Xu, Hui Yan, Qifeng Fan
Institution: Guangdong Baiyun University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414182173
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