Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM
| Autor: | Babak Loni, Anne Schuth, Chia-Lun Yeh |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 02 engineering and technology computer.software_genre SemEval Task (project management) Support vector machine Ranking 020204 information systems Classifier (linguistics) Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | SemEval@NAACL-HLT |
| DOI: | 10.18653/v1/s19-2187 |
| Popis: | In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60% to near 80%. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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