SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction
Authors: Zhengqiu He, Wenliang Chen, Zhenghua Li, Meishan Zhang, Wei Zhang, Min Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve stateof-the-art performance of relation extraction. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Soochow University, China 2Alibaba Group, China 3School of Computer Science and Technology, Heilongjiang University, China |
| Pseudocode | No | The paper describes methods in prose and with diagrams (Figure 2, Figure 3, Figure 4) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No mention or link to open-source code for the methodology. |
| Open Datasets | Yes | We adopt the benchmark dataset developed by Riedel, Yao, and Mc Callum (2010), which has been widely used in many recent works (Hoffmann et al. 2011; Surdeanu et al. 2012; Lin et al. 2016; Ji et al. 2017). Riedel, Yao, and Mc Callum (2010) use Freebase as the distant supervision source and the three-year NYT corpus from 2005 to 2007 as the text corpus. |
| Dataset Splits | No | We tune the hyper-parameters of all the baseline and our proposed models on the training dataset using three-fold validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | Yes | First, we employ the off-shelf Stanford Parser5 to parse the New York Times (NYT) corpus (Klein and Manning 2003). and 5https://nlp.stanford.edu/software/lex-parser.shtml, and the version is 3.7.0 |
| Experiment Setup | Yes | We try {0.1, 0.15, 0.2, 0.25} for the initial learning rate of SGD, {50, 100, 150, 200} for the mini-batch size of SGD, {50, 80, 100} for both the word and the dependency embedding dimensions, {5, 10, 20} for the position embedding dimension, {3, 5, 7} for the convolution window size l, and {60, 120, 180, 240, 300} for the filter number K. We find the configuration 0.2/150/50/50/5/3/240 works well for all the models, and further tuning leads to slight improvement. |