Comments (4)
Approach: Excluded the top 100 common English words from the extracted source code comments to minimize the chance of matching with licenses with more common English words.
But this is not making much difference with the classification. Please suggest other approaches too.
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Another approach sugession:
to identify the file is license relevant or not using a set of keywords( may be a list combining all the license text)
then, we can take the license relevant text from nomos (STRING.in) to find how much match a known file gives.
How it will be different from nomos : the license signature can be easily updated and new signature can be added dynamically.
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or we can try with other probabilistic approaches.
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We shall maintain this in wiki/drive documents that what algorithm should be used in order to get best results
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Related Issues (20)
- build dependencies are not getting install properly
- Build fails HOT 14
- Make the code PEP8 compliant HOT 2
- Run the evaluator command without any 'similarity' parameter HOT 2
- Shift from argparse module to plac command line parser. HOT 3
- Parallelize the evaluator algorithm HOT 5
- [Proposal] Improve the speed of matching HOT 1
- Pipfile: the replacement for requirements.txt HOT 9
- Pandas error at run: pandas.errors.ParserError HOT 1
- ModuleNotFoundError occurs when running after installing with pip. HOT 3
- Broken Link in README.md
- Ability to scan directories HOT 22
- Invalid File Path in Atarashi HOT 4
- List Index Out of Range HOT 3
- FEAT: Increasing the overall performance of Atarashi HOT 1
- Removing third party module in dameruLevenDist agent HOT 2
- Build fails due to import error from nirjas HOT 1
- Error in CommentPreprocessor
- Improve TF-IDF agent by tuning matches threshold HOT 1
- Make evaluation.py more informative
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