Learning Co-Substructures by Kernel Dependence Maximization
Authors: Sho Yokoi, Daichi Mochihashi, Ryo Takahashi, Naoaki Okazaki, Kentaro Inui
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {yokoi, ryo.t, okazaki, inui}@ecei.tohoku.ac.jp, daichi@ism.ac.jp |
| 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. |