Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling
Authors: Dai Dai, Xinyan Xiao, Yajuan Lyu, Shan Dou, Qiaoqiao She, Haifeng Wang6300-6308
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results show that our framework performances significantly better on extracting overlapping relations as well as detecting long-range relation, and thus we achieve state-of-the-art performance on two public datasets. |
| Researcher Affiliation | Collaboration | Dai Dai,1 Xinyan Xiao,1 Yajuan Lyu,1 Shan Dou,2 Qiaoqiao She,1 Haifeng Wang1 1Baidu Inc., Beijing, China 2Department of Automation, Tsinghua University, Beijing, China |
| Pseudocode | No | The paper describes the model architecture and provides mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology described in this paper, nor does it explicitly state that its code is being released. The links provided are for external datasets used in their experiments. |
| Open Datasets | Yes | We use two public datasets to demonstrate the effectiveness of our method: (1) NYT (Riedel, Yao, and Mc Callum 2010) is a news corpus... We use the same dataset1 published by (Ren et al. 2017). (2) Wiki-KBP (Xiao and Weld 2012) utilizes 1.5M sentences... We use the public training data2 which are automatically labeled using distant supervision and handcrafted patterns by the author of (Liu et al. 2017). |
| Dataset Splits | Yes | We exclude all triplets with relation type of None (because we do not require them as negative samples) and create a validation set by randomly sampling 10% sentences from test set as previous studies (Ren et al. 2017; Zheng et al. 2017b) did. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running its experiments. It only mentions 'Comparison of Running Time' but no hardware specifics. |
| Software Dependencies | No | The paper mentions software components and techniques like 'Bi-LSTM', 'CNN', 'CRF', and 'Adam' optimizer, but it does not specify version numbers for any libraries, frameworks, or languages (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For both datasets, the word embeddings are randomly initialized with 100 dimensions and the character embeddings are randomly initialized with 50 dimensions. The window size of CNN is set to 3 and the number of filters is 50. For Bi-LSTM encoder, the hidden vector length is set to 200. We use l2 regularization with a parameter of 0.001 to avoid overfitting. Parameter optimization is performed using Adam (Kingma and Ba 2014) with learning rate 0.001 and batch size 16. |