Cross-Lingual Entity Linking for Web Tables
Authors: Xusheng Luo, Kangqi Luo, Xianyang Chen, Kenny Zhu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {freefish 6174, luokangqi, st tommy}@sjtu.edu.cn, kzhu@cs.sjtu.edu.cn |
| 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. |