Summary of Mucai at Wojoodner 2024: Arabic Named Entity Recognition with Nearest Neighbor Search, by Ahmed Abdou et al.
mucAI at WojoodNER 2024: Arabic Named Entity Recognition with Nearest Neighbor Search
by Ahmed Abdou, Tasneem Mohsen
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper introduces Arabic KNN-NER, a Named Entity Recognition (NER) model that excels in identifying and categorizing entities in Arabic text. NER is a challenging task in Natural Language Processing (NLP), particularly when applied to Arabic data due to its morphological complexities, lack of capitalization cues, and spelling variants. The authors fine-tune their model using K-Nearest Neighbors (KNN) search over the cached training data, resulting in an impressive 91% accuracy on the WojoodFine dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Arabic KNN-NER is a special computer program that can find specific words or phrases in Arabic texts. It’s really good at doing this task! The program has to deal with some tricky things about Arabic language, like how words change shape when you add different endings. The authors used a clever trick called K-Nearest Neighbors to make their program better. They did very well on a test, getting 91% correct! |
Keywords
* Artificial intelligence * Named entity recognition * Natural language processing * Ner * Nlp
