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].
Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy
Authors: Jiaqing Liang, Yanghua Xiao, Yi Zhang, Seung-won Hwang, Haixun Wang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we systematically evaluate the effectiveness and ef๏ฌciency of the models and solutions proposed in previous sections. ... We evaluate the precision, recall, and running time of different solutions: Precision is the proportion of the truly wrong is A relations in all detected wrong is A relations. ... The experimental result on Probase is shown in Table 4. ... The result is shown in Table 6. |
| Researcher Affiliation | Collaboration | Jiaqing Liang Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University ... Yanghua Xiao Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University Xiaoi Research, Shanghai, China ... Yi Zhang School of Computer Science, Fudan University ... Seung-won Hwang Yonsei University ... Haixun Wang Facebook, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this work, we use a state-of-the-art, data-driven taxonomy, Probase (Wu et al. 2012), as an example for taxonomy cleansing. ... We repeat Exp 1 on another auto-constructed taxonomy Wiki Taxonomy (Ponzetto and Strube 2008), which is a taxonomy auto-constructed from Wikipedia corpus. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We run all solutions on a server with Intel(R) Xeon(R) E5-2632 CPU and 128GB RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |