CUNY-CL / CUNY-CL/wikipron

Nested lists for subdialect info

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bug
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Python
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Description

I noticed this problem for [Armenian](https://en.wiktionary.org/wiki/%D5%A5%D6%80%D5%AF%D6%80%D5%B8%D6%80%D5%A4) and a colleague told me it's also found in [Portuguese](https://en.wiktionary.org/wiki/afetar). For some languages, the pronunciation entry can use a nested list, such that

* The first level contains the main dialect name
* The second level contains a subdialect name

For example, Portuguese <[afetar](https://en.wiktionary.org/wiki/afetar)> has a level-1 entry for (standard) Brazilian Portuguese. But this entry has 3 level-2 entries for different regions of Brazil. As of now, WikiPron scraps all 4 [pronunciations](https://github.com/kylebgorman/wikipron/blob/master/data/tsv/por_bz_phonemic_filtered.tsv) as part of "Brazilian Portuguese". But that obfuscates the fact that the 4 entries correspond to separate subdialects.

It would be nice if the script could 'fix' this somehow. Maybe you can add an extra column to the scraped content, such that the new column would keep the name of its line-entry's name. For example, for afetar, maybe you could return something like

afetar | a f e t a ɹ | Brazil
afetar | a f e t a ɻ | Paulista
afetar | a f e t a ʁ | South Brazil
afetar | a f e t a χ | Carioca

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by tracing the scraper output that produces data/tsv/por_bz_phonemic_filtered.tsv and inspect how nested pronunciation entries are represented. Define how subdialect names should be preserved in the scraped output while retaining the existing pronunciation data, then verify the result against the Portuguese afetar example and the Armenian case.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
30/100

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