Biased Random Walk based Social Regularization for Word Embeddings
Authors: Ziqian Zeng, Xin Liu, Yangqiu Song
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our random walk based social regularizations perform better on sentiment classification. |
| Researcher Affiliation | Academia | 1Department of CSE, The Hong Kong University of Science and Technology 2School of Data and Computer Science, Sun Yat-sen University |
| Pseudocode | Yes | Algorithm 1 SWE with regularization using Random Walks. Algorithm 2 Social Regularization |
| Open Source Code | Yes | The code is available at https://github.com/HKUSTKnowComp/SRBRW. |
| Open Datasets | Yes | We conducted all experiments on Yelp Challenge1 datasets which provide a lot of review texts along with large social networks. 1 https://www.yelp.com/dataset_challenge |
| Dataset Splits | Yes | We randomly split data to be 8:1:1 for training, developing, and testing identically for both training word embeddings and downstream tasks, in which we ensure that reviews published by the same user can be distributed to training, development, and test sets according to the proportion. |
| Hardware Specification | Yes | We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan GPU used for this research. |
| Software Dependencies | No | The paper mentions software like 'word2vec', 'CBOW', and 'Lib Linear', but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Finally, we set β = 0.5 in Yelp Round 9, β = 1.0 in Yelp Round 10, and l = 60, n = 10, p = 0.5, q = 1, α = 0.12 , λ = 8.0, r2 = 0.25 in both datasets. Unless we test the parameter sensitivity of our algorithms, we will fix all the hyper-parameters for the following experiments. |