Ordering Concepts Based on Common Attribute Intensity

Authors: Tatsuya Iwanari, Naoki Yoshinaga, Nobuhiro Kaji, Toshiharu Nishina, Masashi Toyoda, Masaru Kitsuregawa

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on real-world concepts revealed a strong correlation between orderings obtained by our methods and gold-standard orderings.
Researcher Affiliation Collaboration Tatsuya Iwanari The University of Tokyo... Naoki Yoshinaga Institute of Industrial Science, the University of Tokyo... Nobuhiro Kaji Yahoo Japan Corporation... Toshiharu Nishina Rakuten Inc.... Masashi Toyoda Institute of Industrial Science, the University of Tokyo... Masaru Kitsuregawa National Institute of Informatics Institute of Industrial Science, the University of Tokyo
Pseudocode No The paper describes methods textually and with mathematical formulas but does not include any structured pseudocode or algorithm blocks.
Open Source Code Yes We will release the evaluation dataset (Table 1) with human orderings and the experimental codes for the academic and industrial communities at http://www.tkl.iis.u-tokyo.ac.jp/ nari/ijcai-16/ to facilitate the reproducibility of our results and their use in various application contexts.
Open Datasets Yes We will release the evaluation dataset (Table 1) with human orderings and the experimental codes for the academic and industrial communities at http://www.tkl.iis.u-tokyo.ac.jp/ nari/ijcai-16/ to facilitate the reproducibility of our results and their use in various application contexts.
Dataset Splits Yes We conducted leave-one-out cross-validation using the evaluation dataset described in Section 4.1.
Hardware Specification No The paper does not provide any specific details about the hardware used for running experiments.
Software Dependencies No The paper mentions using J.Dep P and LIBLINEAR but does not provide specific version numbers for these software dependencies. For example, 'We used LIBLINEAR [Fan et al., 2008]4 as implementations of ranking SVM and SVR'.
Experiment Setup No The paper mentions that hyperparameters were 'tuned by cross-validation on training data' but does not explicitly provide specific hyperparameter values, training configurations, or system-level settings in the main text.