Hierarchy Prediction in Online Communities
Authors: Denys Katerenchuk, Andrew Rosenberg
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose to develop an algorithm that brings together a state-of-the-art NLP research from multiple subareas to enhance hierarchy prediction in online communications. Our proposed algorithm relies on data from different NLP domains. Combining these theories into a single model will improve the best hierarchy prediction task algorithms. |
| Researcher Affiliation | Academia | Denys Katerenchuk and Andrew Rosenberg CUNY Graduate Center, New York, USA 365 Fifth Avenue, Room 4319, New York, NY 10016 CUNY Queens College, New York, USA 65-30 Kissena Boulevard, Room A-202, Flushing, NY 11367 |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The paper states, 'For this reason, we collect data from Reddit1.' with a footnote '1www.reddit.com'. This indicates data was collected from a public platform, but the specific dataset collected and used by the authors is not explicitly stated to be publicly available with access information (e.g., a specific download link or repository for their collected data). |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., training, validation, test percentages or counts) or cross-validation methodology. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, or cloud computing specifications) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |