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..
Did You Know? — Mining Interesting Trivia for Entities from Wikipedia
Authors: Abhay Prakash, Manoj Kumar Chinnakotla, Dhaval Patel, Puneet Garg
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that our rich set of features indeed help in surfacing interesting trivia in the top ranks. |
| Researcher Affiliation | Collaboration | Abhay Prakash1, Manoj K. Chinnakotla2, Dhaval Patel1, Puneet Garg2 1Indian Institute of Technology, Roorkee, India 2Microsoft, India EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We harness publicly available user-generated trivia data from IMDB. |
| Dataset Splits | Yes | We tuned the kernel, model parameters C and e using five-fold cross validation and rest of the values were set to default. The best parameters were found to be a linear kernel with C = 17 and e = 0.21. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, processors, or memory) used for running its experiments. |
| Software Dependencies | No | We used the SV M rank package [Joachims, 2006] for implementing the interestingness ranker. |
| Experiment Setup | Yes | We tuned the kernel, model parameters C and e using five-fold cross validation and rest of the values were set to default. The best parameters were found to be a linear kernel with C = 17 and e = 0.21. |