Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective
Authors: Huang Xu, Zhiwen Yu, Bin Guo, Mingfei Teng, Hui Xiong
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method. |
| Researcher Affiliation | Academia | Huang Xu1, Zhiwen Yu1, Bin Guo1, Mingfei Teng2, Hui Xiong2 1 Northwestern Polytechnical University, Xi an, Shaanxi, 710129 China 2 Rutgers University |
| Pseudocode | Yes | Algorithm 1: Stochastic job title hierarchy extraction |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We have collected 20,243,120 digital resumes from a large OPN until October 2017. (The paper describes collecting a dataset but does not provide specific access information like a link, DOI, or a citation to a publicly available version.) |
| Dataset Splits | No | Hyperparameters are tuned on 5% of the dataset. (While it mentions using 5% of the dataset for hyperparameter tuning, it does not explicitly provide the full dataset split information (e.g., specific percentages for training, validation, and test sets) to reproduce the experiment’s data partitioning.) |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions that “Hyperparameters are tuned on 5% of the dataset” but does not provide specific values for hyperparameters or other detailed training configurations in the main text. |