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..
What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM
Authors: Linmei Hu, Juanzi Li, Liqiang Nie, Xiao-Li Li, Chao Shao
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a real-world dataset demonstrate the superiority of our model over several state-of-the-art methods. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Technology, Tsinghua University, China 2 School of Computer Science and Technology, Shandong University, China 3 Institute for Infocomm Research, A*STAR, Singapore |
| Pseudocode | No | The paper describes the model architecture and steps but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We therefore crawled a large-scale Chinese news event dataset containing 15,254 news series from Sina News2. 2http://news.sina.com.cn/zt/ |
| Dataset Splits | Yes | After preprocessing, we randomly split all the events into three parts: 80% for training, 10% for validation and the remaining 10% for test. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "ICTCLAS3" and "SRILM tool (Stolcke and others 2002)" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The optimal parameter values are given as follows. 1) LSTM parameters and word embedding were initialized from a uniform distribution between [-0.08, 0.08]; 2) Learning rate = 0.1; 3) Batch size = 32; 4) Dropout rate = 0.2; 5) The dimension of word embeddings and topic embeddings = 100, and the dimension of hidden vector D = 400; 6) The number of hidden layers of the LSTM networks = 2; 7) The topic number = 1,000. |