- PLOD-AbbreviationDetection VS medmcqa
- PLOD-AbbreviationDetection VS ThoughtSource
- PLOD-AbbreviationDetection VS hate-speech-and-offensive-language
- PLOD-AbbreviationDetection VS nlp
- PLOD-AbbreviationDetection VS converse
- PLOD-AbbreviationDetection VS noisy-sentences-dataset
- PLOD-AbbreviationDetection VS pytorch-sentiment-analysis
- PLOD-AbbreviationDetection VS adaptnlp
- PLOD-AbbreviationDetection VS transformers-interpret
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Clustering to find abbreviations
Finally, the main problem with unsupervised learning is that you won't be able to reliably measure system performance or improvement. In my view, any time you can spend annotating and collecting data for a (semi-)supervised solution will be well-spent. Existing datasets can also get you started with model development, such as https://github.com/surrey-nlp/PLOD-AbbreviationDetection. Once you have a good model on a conventional dataset, you should be able to start generalizing it to your specific task/dataset.
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0.0
almost 4 years ago
surrey-nlp/PLOD-AbbreviationDetection is an open source project licensed under Creative Commons Attribution Share Alike 4.0 which is not an OSI approved license.
The primary programming language of PLOD-AbbreviationDetection is Jupyter Notebook.
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