Cross-Lingual Taxonomy Alignment with Bilingual Biterm Topic Model
Authors: Tianxing Wu, Guilin Qi, Haofen Wang, Kang Xu, Xuan Cui
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two kinds of real world datasets. The experimental results show that our approach significantly outperforms the designed state-of-the-art comparison methods. |
| Researcher Affiliation | Academia | 1 Key Laboratory of Computer Network and Information Integration of State Education Ministry, School of Computer Science and Engineering, Southeast University, China {wutianxing, gqi, kxu, xcui}@seu.edu.cn 2 East China University of Science & Technology, China whfcarter@ecust.edu.cn |
| Pseudocode | Yes | Algorithm 1: Generative Process of Bi BTM |
| Open Source Code | No | The paper mentions a public GitHub link, but it specifies that the link is for 'two different kinds of real world datasets, which are publicly available2. 2https://github.com/jxls080511/080424'. There is no statement indicating that the source code for the methodology is available. |
| Open Datasets | Yes | We evaluated our proposed approach on two different kinds of real world datasets, which are publicly available2. 2https://github.com/jxls080511/080424 |
| Dataset Splits | No | The paper describes how ground truth data was generated ('five annotators labelled the most relevant category... for each of the 100 randomly selected categories') and mentions '500 iterations of Gibbs sampling to converge' but does not specify train/validation/test dataset splits for model training or evaluation, nor does it refer to standard predefined splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like 'Google Translate' and 'Fudan NLP (Qiu, Zhang, and Huang 2013)' but does not provide specific version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | In Bi BTM, we set α = 50/K, β = 0.1 and K = 120 (the empirical tuning results will be presented in Section 4.2.2). In Bi LDA, we set α = 50/K, β = 0.1 and K = 80 (the empirical tuning results will be presented in Section 4.2.2). For each model, we ran 500 iterations of Gibbs sampling to converge. |