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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Co-Substructures by Kernel Dependence Maximization
Authors: Sho Yokoi, Daichi Mochihashi, Ryo Takahashi, Naoaki Okazaki, Kentaro Inui
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report the results of empirical evaluations, in which the proposed method is applied for acquiring and predicting narrative event pairs, an active task in the field of natural language processing. |
| Researcher Affiliation | Academia | 1 Tohoku University, Sendai, Japan 2 The Institute of Statistical Mathematics, Tokyo, Japan EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms but does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to source code or explicitly state that the code for the methodology is being released. |
| Open Datasets | Yes | We used the following two corpora: The Gigaword Corpus5 [Graff and Cieri, 2003]: a large collection of English newswire text data... Andrew Lang Fairy Tale Corpus6: a small collection of children’s stories... 5https://catalog.ldc.upenn.edu/ldc2003t05/ 6http://www.mythfolklore.net/andrewlang/ |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention or detail a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | Yes | Applying Stanford Core NLP Version 3.7.0 [Manning et al., 2014] to raw text from the corpora, we extracted sentence pairs sharing co-referring arguments. |
| Experiment Setup | Yes | We ran the MH sampler with β = 108 to draw 7 × 105 and 2 × 105 samples, respectively, for the Gigaword corpus the Fairy Tale corpora. |