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..
Cross-Lingual Entity Linking for Web Tables
Authors: Xusheng Luo, Kangqi Luo, Xianyang Chen, Kenny Zhu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results report that our approach improves the accuracy of cross-lingual table linking by a relative gain of 12.1%. Detailed analysis of our approach also shows a positive and important gain brought by the joint framework and coherence feature. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road, Shanghai, China 200240 EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Local-Search Descent Prediction Input: Mention table X, linking position P, initial entity table E0, candidate generator Cand( ), scoring function S( , ) Output: Entity table E |
| Open Source Code | No | The paper does not provide an explicit statement or link to the source code for the described methodology. A link to the dataset is provided, but not the code. |
| Open Datasets | Yes | The dataset is available at https://adapt.seiee.sjtu.edu.cn/tabel |
| Dataset Splits | Yes | We randomly split the dataset8 into training / validation / testing sets (80 : 20 : 50 tables). |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like Word2Vec, Rank Net, and Adam, but does not provide specific version numbers for any of them. For example, it states: "We adopt Word2Vec (Mikolov et al. 2013) to learn the initial embeddings" and "Here we adopt Rank Net (Burges 2010) with Adam stochastic optimizier (Kingma and Ba 2014) as our implementation." |
| Experiment Setup | Yes | For all the variations of our approach, we set Ncand = 30, Ntab = 49, dcell = dcont = 100, dout = 200, η = 0.0002 and p = 0.9 under Rank Net optimizer, as reaching the highest Micro Accuracy in the validation set. |