Multi-Labeled Relation Extraction with Attentive Capsule Network
Authors: Xinsong Zhang, Pengshuai Li, Weijia Jia, Hai Zhao7484-7491
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works. |
| Researcher Affiliation | Academia | Xinsong Zhang,1 Pengshuai Li,1 Weijia Jia,2,1 Hai Zhao1 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2State Key Lab of Io T for Smart City, University of Macau, Macau 999078, China {xszhang0320, pengshuai.li}@sjtu.edu.cn and {jia-wj, zhaohai}@cs.sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Attention-based Routing Algorithm |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | We conduct experiments on two widely used benchmarks for relation extraction, NYT-10 (Riedel, Yao, and Mc Callum 2010) and Sem Eval-2010 Task 8 dataset (Hendrickx et al. 2009). The NYT-10 dataset is generated by aligning Freebase relations with the New York Times (NYT) corpus, in which sentences from the years 2005-2006 are for training while those from 2017 for testing. The dataset consists of amounts of multi-labeled sentences. The Sem Eval-2010 Task 8 dataset is a small dataset which has been well-labeled for relation extraction. The details of both datasets are shown in Table 1. |
| Dataset Splits | No | The paper does not explicitly provide validation dataset splits. While it mentions train/test splits and 10-fold cross-validation in evaluation, specific validation set details are missing. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are provided for running the experiments. |
| Software Dependencies | No | Software components like word2vec and Adam optimizer are mentioned, but specific version numbers for any libraries or frameworks are not provided. |
| Experiment Setup | Yes | Table 2 lists our hyper-parameter setting. Parameters NYT-10 Sem. batch size 50 50 word dimension p 50 50 position dimension q 5 5 hidden state dimension sh 256 256 capsule dimensions [du, dr] [16,16] [16,16] iterations z 3 3 sliding-margin γ 0.4 0.4 down-weighting λ 1.0 0.5 learning rate 0.001 0.001 dropout probability 0.0 0.7 L2 regularization strength 0.0001 0.0 |