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..
Improving Context and Category Matching for Entity Search
Authors: Yueguo Chen, Lexi Gao, Shuming Shi, Xiaoyong Du, Ji-Rong Wen
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the INEX 2009 entity ranking task show that the proposed approach achieves a significant improvement of the entity search performance (xinf AP from 0.27 to 0.39) over the existing solutions. |
| Researcher Affiliation | Collaboration | Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, China School of Information, Renmin University of China Microsoft Research Asia, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes models and formulas but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We adopt a public-available document collection in our experiments: Wikipedia INEX 2009 collection2 (shorted as INEX09). 2http://www.mpi-inf.mpg.de/departments/d5/software/inex/ |
| Dataset Splits | No | The paper mentions using the INEX 2009 entity ranking task dataset but does not specify the training, validation, or test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for conducting the experiments. |
| Software Dependencies | No | The paper mentions using the 'Wikipedia-Miner (Milne and Witten 2008)' toolkit but does not specify version numbers for it or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | For the parameter h (L uses it for retrieving top-h relevant documents of a topic), we set h = 300 by default for LRCM and SRCM because larger h can only improve the precision very slightly. For the parameter λ, we adjust it and plot the results of L, S, LC and SC in Figure 1 respectively. It can be seen that the precision is stable for a wide range of λ. As such, we simply set λ = 0.5 for all the other experiments. For the parameter k of LCR+SCR, we find that the best performance is achieved when k = 20 in our experiments. |