We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| daojiaxu/semeval_11 | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| KMP_algorithm | Ran | daojiaxu/semeval_11/KMP.py code served (permissive licence) · get_code("df794508afaec4e2") |
| gen_pnext | Ran | daojiaxu/semeval_11/KMP.py code served (permissive licence) · get_code("fd9d811ea9b94a0f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro-F1-score for the SI subtask is 0.406, and the micro-F1-score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques. The code of this paper is availabled at: https://github.com/daojiaxu/semeval_11.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.10166")
get_code_for_paper("2008.10166")
have("2008.10166")
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