ThoughtSource
PLOD-AbbreviationDetection
| ThoughtSource | PLOD-AbbreviationDetection | |
|---|---|---|
| 1 | 1 | |
| 844 | 9 | |
| 1.5% | - | |
| 8.4 | 0.0 | |
| 10 months ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Creative Commons Attribution Share Alike 4.0 |
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ThoughtSource
PLOD-AbbreviationDetection
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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.
What are some alternatives?
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