Matching with Synonyms using KeyLLM OR KeyBERT
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- Python
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Description
I have been playing with KeyBERT and KeyLLM for a while now. And here is something I would like to achieve.
If have a text "CO2 emissions are high these days" and a list of candidate words, which might contain the word Carbondioxide and not CO2 will KeyBERT or KeyLLM find Carbondioxide as a match?
Text = "CO2 emissions are high these days"
candidate keyword list have the word ["Carbon dioxide"] and not "CO2"
Expected output = ["Carbon dioxide"]
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No source file or test is identified in the issue. Start by reviewing the KeyBERT and KeyLLM candidate-keyword behavior for the CO2 and "Carbon dioxide" example, then define done as determining whether the candidate is matched through synonym handling and documenting or implementing the required behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100