Position-aware Joint Entity and Relation Extraction with Attention Mechanism
Authors: Chenglong Zhang, Shuyong Gao, Haofen Wang, Wenqiang Zhang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our model is effective. With the same pre-trained encoder, our model achieves the new state-of-the-art on standard benchmarks (ACE05, Co NLL04 and Sci ERC), obtaining a 4.7%-17.8% absolute improvement in relation F1. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China 2Academy for Engineering & Technology, Fudan University, Shanghai, China 3College of Design and Innovation, Tongji University, Shanghai, China |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use three popular relation extraction datasets: ACE05, Co NLL04 and Sci ERC. Table 2 shows the statistical information of each dataset. The ACE05 dataset consists of English, Arabic, and Chinese data collected from various domains, such as newswire and online forums. [...] For the Co NLL04 dataset, we adopt the training set (1,153 sentences) and test set (288 sentences) split by [Gupta et al., 2016]. |
| Dataset Splits | Yes | To tune the hyperparameters, 20% of the training set is used as the development set. |
| Hardware Specification | No | The paper mentions using pre-trained models like "bert-base-uncased" and "albert-xxlarge-v2" but does not specify the hardware (e.g., GPU, CPU models, memory) used for training or experimentation. |
| Software Dependencies | No | The paper mentions the use of pre-trained models like "bert-base-uncased" and "albert-xxlarge-v2", but does not list specific software dependencies with version numbers (e.g., Python version, PyTorch version). |
| Experiment Setup | Yes | For datasets ACE05, Co NLL04 and Sci ERC, we set the maximum length of the extended sentences to 256. We consider spans up to L = 8 words. On the data sets ACE05, Co NLL04 and Sci ERC, we take the retention factor λ = 0.05. |