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 [1].
Tweet Timeline Generation with Determinantal Point Processes
Authors: Jin-ge Yao, Feifan Fan, Wayne Xin Zhao, Xiaojun Wan, Edward Chang, Jianguo Xiao
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems. |
| Researcher Affiliation | Collaboration | Jin-ge Yao1,2, Feifan Fan1, Wayne Xin Zhao3, Xiaojun Wan1,2, Edward Chang4, Jianguo Xiao1,2 1Institute of Computer Science and Technology, Peking University, Beijing 100871,China 2Key Laboratory of Computational Linguistic (Peking University), MOE, China 3School of Information, Renmin University of China 4HTC Research, Beijing, China |
| Pseudocode | Yes | Algorithm 1 A greedy algorithm for DPP model |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology described. |
| Open Datasets | Yes | We evaluate the proposed TTG systems over 55 of๏ฌcial topics in the TREC 2014 Microblog track (Lin and Efron 2014). |
| Dataset Splits | Yes | Topics in TREC 2011-2012 are used as the development set for parameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For spectral rescaling, we simply set ฮฑ = 0.5. |