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@@ -18,7 +18,7 @@ ChouBERT-n-plant-health-tweet-classifier are fine-tuned ChouBERT-n for distingui
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  Our work shows that ChouBERT-16 and ChouBERT-32-based classifiers are the most generalizable for recognizing unseen hazards, especially polysemous terms.
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  We also upload the CamemBERT-based classifiers as the baseline.
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- ### BibTeX entry
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  ```bibtex
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  @inproceedings{jiang2022choubert,
@@ -29,4 +29,15 @@ We also upload the CamemBERT-based classifiers as the baseline.
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  year={2022},
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  organization={Springer}
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  }
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  Our work shows that ChouBERT-16 and ChouBERT-32-based classifiers are the most generalizable for recognizing unseen hazards, especially polysemous terms.
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  We also upload the CamemBERT-based classifiers as the baseline.
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+ ### BibTeX entries
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  ```bibtex
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  @inproceedings{jiang2022choubert,
 
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  year={2022},
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  organization={Springer}
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  }
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+
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+ @inproceedings{jiang2022ner,
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+ title = {{Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach}},
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+ author = {Jiang, Shufan and Angarita, Rafael and Cormier, St{\'e}phane and Rousseaux, Francis},
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+ booktitle = {{2022 6th International Conference on Universal Village (UV)}},
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+ address = {Boston, United States},
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+ publisher = {{IEEE}},
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+ year = {2022},
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+ doi = {10.1109/UV56588.2022.10185492},
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+ }
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+
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  ```