Auto-sentence breaker
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- Dominant language
- Python
- Stars
- 39
- Forks
- 7
- Avg merge
- 1d 9h
- Merged PRs (30d)
- 5
Description
Is this even possible? Can we with minimal a priori knowledge can we separate sentences in all languages in all scripts enough so that when combined with a Gale-Church sentence Aligner, we can get decent training and translation data within our 200 token maximum? It doesn't have to be perfect - and splitting up sentences more than they should be may be ok. The main issues are - what will we find in different languages in terms of sentence ending punctuation?
First proposal idea:
- Have knowledge of the sentence terminating characters per script
- Analyze the incoming text to determine the script (or use the script code in the language code)
- Follow some basic rule-based knowledge:
- Refer to https://github.com/uhermjakob/utoken/tree/main for many rules that may span scripts (emails, URL's)
- If under 200? tokens, keep parenthesis and brackets unified
- Are there abbreviations in other scripts that also use the sentence terminating character? (D. L. Moody, etc.). If we know a bit about the types of things we could find, we may be able to develop some statistics-rule based determination for sentence breaks
- Use ebible data to test out algorithms
Contributor guide
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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
Start by reviewing the proposed rules, the utoken rules referenced in the issue, and the Gale-Church sentence aligner context. Use ebible data to evaluate sentence breaking across scripts, including sentence-ending punctuation, abbreviations, brackets, and the 200-token limit. Done means a tested approach that produces usable sentence-alignment data, though the issue does not name files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100