Summary of Computational Approaches to Arabic-english Code-switching, by Caroline Sabty
Computational Approaches to Arabic-English Code-Switching
by Caroline Sabty
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes solutions for Named Entity Recognition (NER) tasks on code-switched Arabic-English data, which is essential for tackling challenges in multilingual NLP. The authors create an annotated corpus for the NER task on CS data and apply state-of-the-art techniques to improve the performance of the NER taggers. They also develop enhancement methods using contextual embeddings and data augmentation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper focuses on solving a big problem in computer science – helping computers understand mixed language conversations, like code-switching between Arabic and English. Code-switching is very common, especially online, and it’s hard to analyze because it mixes different languages together. The authors created new ways to recognize named entities (like names or locations) in this type of data and improved their methods using special techniques. |
Keywords
» Artificial intelligence » Data augmentation » Named entity recognition » Ner » Nlp
