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..
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
Authors: Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct three experiments on a widely used dataset and the experimental results show that our approach outperforms all the baseline systems significantly. |
| Researcher Affiliation | Academia | Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China EMAIL |
| Pseudocode | No | The paper describes its models and formulas but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide a specific link to the source code for its methodology or explicitly state that the code is being released. |
| Open Datasets | Yes | We evaluate our approach using the dataset developed by (Riedel, Yao, and Mc Callum 2010) by aligning Freebase5 relations with the New York Times (NYT) corpus. |
| Dataset Splits | Yes | In our experiments, we tune all of the models using three-fold validation on the training set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions training word embeddings with 'word2vec' and provides a general link, but it does not specify version numbers for word2vec or any other ancillary software components. |
| Experiment Setup | Yes | The best configurations are: kw = 50, kd = 5, w = 3, n = 200, λ = 0.01, the batch size is 50. Following (Hinton et al. 2012), we set the dropout rate 0.5. |