Did You Know? — Mining Interesting Trivia for Entities from Wikipedia

Authors: Abhay Prakash, Manoj Kumar Chinnakotla, Dhaval Patel, Puneet Garg

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 abhayprakash@outlook.com, {manojc,puneetga}@microsoft.com, patelfec@iitr.ac.in
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.