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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In this section, we systematically evaluate the effectiveness and efficiency 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.