Understanding Emerging Spatial Entities
Authors: Jinyoung Yeo, Jin-woo Park, Seung-won Hwang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with state-of-the-art baselines, our proposed approach improves recall and F1 score by up to 24% and 18%, respectively. To show the effectiveness and robustness of our approach, we have conducted extensive experiments in three different cities, Seattle, Washington D.C., and Taipei, of varying characteristics such as geographical density and language. |
| Researcher Affiliation | Academia | Jinyoung Yeo, Jin-woo Park Pohang University of Science and Technology (POSTECH) {jinyeo, jwpark85}@postech.ac.kr Seung-won Hwang Yonsei University seungwonh@yonsei.ac.kr |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement or a link to indicate that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes collecting data from Flickr API and Trip Advisor to create its datasets, but it does not provide specific access information (link, DOI, repository) for the processed datasets used in the experiments. |
| Dataset Splits | Yes | We adopt a 3-fold cross validation by randomly partitioning the ground truth into three similarly sized groups. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions external APIs (Flickr API, Geocode Dataflow API) used for data collection, but it does not list any specific software dependencies or libraries with version numbers required to replicate their experimental setup or methodology. |
| Experiment Setup | Yes | In the training step of all systems, we set the objective function to maximize F1 score while setting the minimum allowable precision to 0.8. and λ (0, 1) is a systematic parameter, which is determined using the quality function (F1 score in Eq. 9) on the training data set; it is experimentally set to 0.5 in our work. |