Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations
Authors: Changzhi Sun, Yuanbin Wu7039-7046
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the benchmark NYT dataset show that our approach significantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Changzhi Sun,1 Yuanbin Wu1,2 1School of Computer Science and Software Engineering, East China Normal University 2Shanghai Key Laboratory of Multidimensional Information Processing changzhisun@stu.ecnu.edu.cn, ybwu@cs.ecnu.edu.cn |
| Pseudocode | Yes | Algorithm 1 The training procedure |
| Open Source Code | No | 1We will make our implementation publicly available. |
| Open Datasets | Yes | We evaluate the proposed framework on public NYT dataset. 5The training set has 353k relation triples, which are generated by distant supervision. ... 5https://github.com/shanzhenren/Co Type. We use standard ACE05 dataset as the manually labeled dataset (see Figure 1) 6. ... 6https://github.com/tticoin/LSTM-ER. |
| Dataset Splits | Yes | Following (Ren et al. 2017; Zheng et al. 2017; Wang et al. 2018), we randomly select 10% of the test set as the development set and use the remaining data as evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory specifications, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'bi LSTM', 'CNNs', 'Adadelta', 'dropout' and 'glove word embeddings' but does not provide specific version numbers for any software, libraries, or frameworks used. |
| Experiment Setup | Yes | Our word embeddings is initialized with 100-dimensional glove (Pennington, Socher, and Manning 2014) word embeddings. The dimensionality of the hidden units is 128. For all CNN in our network, the kernel sizes are 2 and 3, and the output channels are 25. We optimize our model using Adadelta (Zeiler 2012) with gradient clipping. The network is regularized with dropout. |