Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Gazetteer-Independent Toponym Resolution Using Geographic Word Profiles
Authors: Grant DeLozier, Jason Baldridge, Loretta London
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 2 shows test set performance for all models when resolving gold-standard toponyms. |
| Researcher Affiliation | Academia | University of Texas at Austin Austin TX, 78712 EMAIL EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Topo Cluster code and precomputed local statistic calculations are available online https://github.com/grantdelozier/TopoCluster |
| Open Datasets | Yes | For this, we use Geo Wiki, the subset of Wikipedia pages that contain latitude-longitude pairs in their info box. ... We use two corpora used previously by (Speriosu and Baldridge 2013): TR-Co NLL (Leidner 2008) and CWar (Speriosu 2013). ... The Local-Global Lexicon corpus (LGL) was developed by (Lieberman, Samet, and Sankaranarayanan 2010) |
| Dataset Splits | Yes | TR-Co NLL was split by Speriosu and Baldridge (2013) into a dev (4,356 Toponyms) and a held-out test set (1,903 Toponyms). ... We use the same split of CWar as (Speriosu and Baldridge 2013): dev (157,000 toponyms) and test (85,000 toponyms). |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'Stanford NER s 3-class CRF model' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | A grid search was run on the dev portions of the datasets to derive values of three parameters θ1, θ2, and θ3 corresponding to weights on the g of the main toponym, context toponyms, and other context words, respectively. ... Table 1 shows the values obtained for the respective Model-Domain combinations. |