Entity Suggestion with Conceptual Expanation
Authors: Yi Zhang, Yanghua Xiao, Seung-won Hwang, Haixun Wang, X. Sean Wang, Wei Wang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on real data sets justify the accuracy of our models and the efficiency of our solutions.In this section, we systematically evaluate the effectiveness of our models and solutions with the comparison to the state-ofthe-art approaches using the following two types of datasets.Table 2: Results on SEAL dataset |
| Researcher Affiliation | Collaboration | 1Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China 2Shanghai Internet Big Data Engineering Technology Research Center, China 3Department of Computer Science, Yonsei University, Korea 4Facebook Inc., USA 5Shuyan Technology, China |
| Pseudocode | No | The paper describes the proposed models and computations in text and mathematical formulas but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to source code repositories or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | We use English lists in the SEAL data set [Wang and Cohen, 2007] |
| Dataset Splits | No | The paper does not specify traditional training, validation, and test splits for the datasets in the context of model training and evaluation. It describes how query sets are constructed for evaluation, but not data splits for model learning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct the experiments, such as CPU/GPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers, such as programming languages, libraries, or specialized packages used for the experiments. |
| Experiment Setup | Yes | As we are only interested in the concepts within a short distance, we just ignore concepts with distance larger than a certain threshold of T steps, which reduces the computation cost. In our experiment, we set T to be 3. |