Learning Defining Features for Categories
Authors: Bo Xu, Chenhao Xie, Yi Zhang, Yanghua Xiao, Haixun Wang, Wei Wang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the effectiveness and efficiency of our method. Finally, we find defining features for overall 60,247 categories with acceptable accuracy. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Fudan University, Shanghai, China 2Xiaoi Research, Shanghai, China 3Shanghai Internet Big Data Engineering and Technology Center, Shanghai, China 4Facebook, USA |
| Pseudocode | Yes | Algorithm 1 Extracting C-DFs from DBpedia. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code will be made publicly available. |
| Open Datasets | Yes | We use DBpedia2015-04 version as our dataset. |
| Dataset Splits | No | The paper does not specify explicit training, validation, and test dataset splits (e.g., percentages or counts). It mentions using human judgment on randomly selected samples for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python version, library versions) used for the experiments. |
| Experiment Setup | Yes | The minimum support threshold is set as , equivalent to the goodness score threshold used for the pruning of the unpromising feature sets. Thus, the setting of is critical for both efficiency and effectiveness of our solution. ... As a result, we use 0.6 as the confidence threshold. |