Summary of Text Classification: Neural Networks Vs Machine Learning Models Vs Pre-trained Models, by Christos Petridis
Text Classification: Neural Networks VS Machine Learning Models VS Pre-trained Models
by Christos Petridis
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores text classification techniques, a crucial task in Natural Language Processing (NLP). Building upon the transformer model’s success in machine translation, this study compares seven pre-trained models, three traditional neural networks, and three machine learning models for text classification. The authors investigate two embedding techniques, TF-IDF and GloVe, finding that GloVe consistently outperforms TF-IDF. Notably, pre-trained models like BERT and DistilBERT consistently surpass standard models and algorithms in text classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to sort lots of texts into different categories. This paper looks at how well different methods do this task. It compares many different ways to sort texts, including some really powerful new approaches called transformers. The results show that these new approaches are often much better than older methods. The study also finds that using a special way to turn words into numbers helps make the sorting more accurate. |
Keywords
» Artificial intelligence » Bert » Embedding » Glove » Machine learning » Natural language processing » Nlp » Text classification » Tf idf » Transformer » Translation
