Unaligned Message-Passing and Contextualized-Pretraining for Robust Geo-Entity Resolution
Authors: Yuwen Ji, Wenbo Xie, Jiaqi Zhang, Chao Wang, Ning Guo, Lei Shi, Yue Zhang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method surpasses the baselines, achieving higher F1 scores on 8 real-world geodatasets in terms of robustness, with an improvement of up to 7.9%. The ablation study further justifies our proposal. |
| Researcher Affiliation | Collaboration | Yuwen Ji1,2, Wenbo Xie1*, Jiaqi Zhang1, Chao Wang1, Ning Guo1, Lei Shi2, Yue Zhang3 1Amap, China 2Beihang University, China 3Westlake University, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and equations for UMP and CP (e.g., Eq. (6) to (11)), but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and processed data can be found in this url: https://github.com/2022neo/ger_ump_cp |
| Open Datasets | Yes | Our experiments use three renowned geo-entity databases: Open Street Map (OSM) is a collaborative open-source mapping project with points of interest such as landmarks; Yelp and Foursquare (FSQ) provide user-generated content, offering insights into business, urban mobility, social dynamics. |
| Dataset Splits | Yes | The annotated data is divided into training, validation, and test sets, as detailed in Table 1. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, memory amounts) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions integrating with 'Geo-ER' and 'BERT' but does not provide specific version numbers for these or other software libraries/dependencies (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | For all additional attention layers introduced, the dimensions of the query and key are set to 256 (corresponding to dq and dk in Eq. (6)); the dimension of the value dv = d matches the output feature dimension of the expanded transformer; in Eq. (11), dimension for aggregation d is set to 256, and the activation function σ is set to be sigmoid or softmax. During pretraining, we empirically retrieve neighbors with a random cutoff: we randomly select 50 to 150 of the nearest neighbors within 1000 meters of the pivot entity. The number of algorithm runs for each reported result is 10. Given a perturbation ratio ρ, we randomly select ρ% of attribute/value token positions in all entities for perturbation, replacing tokens with either [MASK] (50%) or random tokens (50%), to simulate value missing and spelling error respectively. |