Summary of Effects Of Term Weighting Approach with and Without Stop Words Removing on Arabic Text Classification, by Esra’a Alhenawi et al.
Effects of term weighting approach with and without stop words removing on Arabic text classification
by Esra’a Alhenawi, Ruba Abu Khurma, Pedro A. Castillo, Maribel G. Arenas
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel study compares the performance of two term weighting strategies, Binary and Term Frequency (TF), on a text classification task for Arabic documents. The authors investigate how these approaches affect classification results in terms of accuracy, recall, precision, and F-measure values when stop words are eliminated or not. The analysis is conducted using an Arabic dataset comprising 322 documents from six main topics. The results show that the TF approach with stop word removal outperforms the Binary approach for all metrics, except for precision where both approaches produce similar results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study compares two ways to help computers understand text: Binary and Term Frequency (TF). They test how these methods work when classifying Arabic documents into different categories. The researchers use a big dataset with 322 documents from six main topics. They want to know which method is better at getting the right answers. The results show that one way is better than the other, but only by a little bit. |
Keywords
* Artificial intelligence * Classification * Precision * Recall * Text classification
