Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Labeled Relation Extraction with Attentive Capsule Network
Authors: Xinsong Zhang, Pengshuai Li, Weijia Jia, Hai Zhao7484-7491
AAAI 2019 | Venue PDF | 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 EMAIL and EMAIL |
| 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 |