Enhancing fine-grained geographic named entity recognition by Multi-scale Siamese Reconstruction Network
Year: 2025
Type: article
Abstract: Fine-grained geographic named entity recognition involves the identification of precise locations within geographic text. Existing methods face challenges in effectively addressing this task, namely: (1) the complexity and long-dependency nature of various fine-grained locations, which pose difficulties for recognition, and (2) the presence of numerous out-of-vocabulary fine-grained locations that are not present in the training data . In this paper, we propose a Multi-scale Siamese Reconstruction Network (MSRN) to tackle these challenges. To address the first challenge, MSRN employs a multi-scale convolutional network to aggregate interactions among coarse-grained and fine-grained span features. To tackle the second challenge, MSRN introduces a siamese reconstruction network that corrupts the representation and then reconstructs it, preventing the model from relying on rote memorization of biased locations in the training data . This approach enhances the model’s understanding of entity context. We conduct experiments on four real-world fine-grained geographic named entity recognition datasets, which contain a variety of complex long fine-grained locations and geographic entities not present in the training data. These experiments illustrate the challenges commonly encountered in real-world engineering applications and deliver a solid foundation for evaluating the robustness and applicability of our proposed method. The experimental results demonstrate the superiority of MSRN over previous models, achieving significant performance improvements ranging from 2.1 to 3.0. Further analysis confirms the effectiveness of MSRN in recognizing complex long-dependency entities, with a performance improvement of 3.0, as well as out-of-vocabulary locations, with a performance improvement of 6.6 compared to models with similar parameter sizes.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
OpenAlex ID: W4408253044