Hierarchical Aligned Multimodal Learning for NER on Tweet Posts
Authors: Peipei Liu, Hong Li, Yimo Ren, Jie Liu, Shuaizong Si, Hongsong Zhu, Limin Sun
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two open datasets, and the results and detailed analysis demonstrate the advantage of our model. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 19 Shucun Road, Haidian District, Beijing 100085 P.R.China 2School of Cyber Security, University of Chinese Academy of Sciences 19 Yuquan Road, Shijingshan District, Beijing 100049 P.R.China |
| Pseudocode | No | The paper includes mathematical equations and architectural diagrams (e.g., Figure 2) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement or a direct link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The experiments are carried out on the datasets TWITTER2015 and TWITTER-2017, which are constructed based on Twitter by (Lu et al. 2018) and (Zhang et al. 2018) separately. |
| Dataset Splits | No | The paper states, 'The experiments are carried out on the datasets TWITTER2015 and TWITTER-2017' and mentions 'Implementation Details' and 'Appendices' for hyperparameters, but it does not explicitly specify the training/validation/test dataset splits (e.g., percentages, counts, or a referenced split methodology) in the provided text. |
| 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 using models like BERT, ResNet, Faster-RCNN, and general components like Transformer and CRF layer, but it does not provide specific version numbers for any software libraries or dependencies used in the implementation. |
| Experiment Setup | No | The paper states, 'For both datasets, we have the same hyperparameters and the specific parameter content can be found in the Appendices.' This indicates that the detailed experimental setup and hyperparameter values are deferred to an external appendix, not explicitly provided within the main body of the paper. |