Employ Transfer Learning Approach for Sentiment Classification at TenPoint7
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Universal Language Model Fine-tuning (ULMFiT)1 is an effective transfer learning approach for solving variety tasks in Natural Language Processing (NLP). The authors shed some light on the three common tasks of text classification: sentiment analysis, question classification, and topic classification. Their work especially achieves state-of-the-art results on six text classification tasks.
- Jeremy Howard and Sebastian Ruder, Universal Language Model Fine-tuning for Text Classification, 2018 ↩