Generating Event Causality Hypotheses through Semantic Relations
Authors: Chikara Hashimoto, Kentaro Torisawa, Julien Kloetzer, Jong-Hoon Oh
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that, from 2.4 million event causalities extracted from the web, our method generated more than 300,000 hypotheses, which were not in the input, with 70% precision. |
| Researcher Affiliation | Academia | National Institute of Information and Communications Technology, Kyoto, 619-0289, Japan { ch, torisawa, julien, rovellia}@nict.go.jp |
| Pseudocode | No | The paper describes its method in prose, but does not include any explicitly labeled pseudocode blocks or algorithms in a structured, code-like format. |
| Open Source Code | No | The paper states: 'We are planning to release generated event causality hypotheses to the public in the near future.' This indicates a future release, not a current availability of the source code for the methodology. |
| Open Datasets | Yes | First, we obtained 2,018,170,662 noun pairs with positive PMI values from the word co-occurrence frequency database (Section 2.1). Then we randomly sampled two million of them. [...] https://alaginrc.nict.go.jp/resources/nict-resource/li-info/lilist.html, ID: A-5 |
| Dataset Splits | No | The paper mentions using 'labeled data consist of 147,519 examples (15,195 are positive)' for training the HYPOCLASSIFIER, but it does not specify a distinct validation split or any other train/validation/test splits for their overall experimental setup or evaluation of generated hypotheses. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, number of machines) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions 'SVM-Light with polynomial kernel d = 2 (svmlight.joachims.org)' and 'J.Dep P (Yoshinaga and Kitsuregawa 2009)' but does not provide specific version numbers for these software components, which is required for reproducibility. |
| Experiment Setup | No | The paper states that the HYPOCLASSIFIER is 'trained by SVM-Light with polynomial kernel d = 2', but it does not provide comprehensive experimental setup details such as learning rates, batch sizes, number of epochs, optimizers, or other specific hyperparameter values for training any models. |