Summary of Part-of-speech Tagger For Bodo Language Using Deep Learning Approach, by Dhrubajyoti Pathak et al.
Part-of-Speech Tagger for Bodo Language using Deep Learning approach
by Dhrubajyoti Pathak, Sanjib Narzary, Sukumar Nandi, Bidisha Som
First submitted to arxiv on: 6 Jan 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 Machine learning has made tremendous progress in processing languages like English and Spanish, but there’s a significant gap when it comes to low-resource languages like Bodo and Mizo. These languages are crucial for millions of people around the world, but they lack the coverage and resources that more prominent languages enjoy. In this study, researchers focus on developing language models and part-of-speech tagging systems for these lesser-studied languages. They introduce BodoBERT, a language model specifically designed for the Bodo language, which is a significant step forward in building AI tools for underrepresented languages. The authors also present an ensemble deep learning-based POS tagging model that combines bi-directional LSTM with CRF and stacked embeddings of BodoBERT with byte-pair embeddings. This innovative approach achieves an impressive F1 score of 0.8041, outperforming other models tested in the experiment. The study’s findings have significant implications for natural language processing research and its applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is trying to help people communicate better by creating special tools for languages that are not well-studied yet. These languages are important because many people speak them. One of these languages is Bodo, which has never had a special AI tool before. The researchers in this study created a language model called BodoBERT just for the Bodo language. They also made a new way to do part-of-speech tagging, which helps computers understand what words mean. The team tested their models and found that one of them did really well on a task called POS tagging. This shows that we can use AI to help people communicate better in languages that are not as popular right now. |
Keywords
* Artificial intelligence * Deep learning * F1 score * Language model * Lstm * Machine learning * Natural language processing
