Summary of Mix Of Experts Language Model For Named Entity Recognition, by Xinwei Chen et al.
Mix of Experts Language Model for Named Entity Recognition
by Xinwei Chen, Kun Li, Tianyou Song, Jiangjian Guo
First submitted to arxiv on: 30 Apr 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 proposed BOND-MoE model is a robust approach for Named Entity Recognition (NER) in natural language processing. By leveraging Mixture of Experts (MoE) and Expectation-Maximization (EM), this model trains multiple NER models and ensembles them to alleviate the issues introduced by distant supervision. The fair assignment module ensures balanced document-model assignments, resulting in state-of-the-art performance on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The BOND-MoE model is a new way to improve named entity recognition in natural language processing. Instead of using one method, it uses multiple methods and combines them to make the results better. This helps fix problems that come from incomplete or noisy data. The model also makes sure each piece of text gets assigned to the right model for best results. This makes the BOND-MoE model very good at recognizing named entities. |
Keywords
» Artificial intelligence » Mixture of experts » Named entity recognition » Natural language processing » Ner
