We've just updated the Data Model with
1. a quick textual hierarchy of the data model from a high level.
2. a few more sentences for "Lemma" bullet point to help explain things a bit better.

(Thanks to a few folks on our Telegram channel)

Take a look!
https://www.wikidata.org/wiki/Wikidata:Lexicographical_data/Documentation#Data_Model



On Wed, Jun 30, 2021 at 2:03 PM Thad Guidry <thadguidry@gmail.com> wrote:
And furthermore... perhaps some small help iconography buttons added for newcomers that must be clicked to see the help info (not hoverable, as that would interfere)
And where the help text would be translatable and the definitions taken from our Data Model and displayed in your Wikidata language preference.
For Example:

Proposed_Lexeme_Page_Tooltip_Bubble.png

- Thad
https://www.linkedin.com/in/thadguidry/
https://calendly.com/thadguidry/


On Wed, Jun 30, 2021 at 1:36 PM Thad Guidry <thadguidry@gmail.com> wrote:
What do folks think of this for a proposed better view of our existing Lexeme page (so that it aligns better with our described Data Model in SVG) to help visualize our data model better on the Lexeme pages themselves?
Does this align with it? Better? Worse? Needs tweaks?

Proposed_Lexeme_Page.png



On Wed, Jun 30, 2021 at 1:33 PM Douglas Clark <clarkdd@gmail.com> wrote:
Agreed mostly. A lexeme is the head word that stands-in for all forms of the same meaning (forms of the same meaning equals lemma or sense). Let's not forget that a lexeme can be more than one word (fire engine, speak up, and even RTFM). From a word perspective, a lexeme is many to many, yet mostly one to many, AND the lexeme as a head word in one repository could also be a lemma of some lexeme in another repository. Author choice. Just wait until you get to the rules of how to select the correct lemma-sense from a lexeme's collection when the clue to the right sense is a sentence four sentences away. It's just going to get more complicated from here. Sadly, Abstract is probably the last large scale manual tagging effort, as there are a plethora of existing tagged corpora that can support Abstract if you would just use a bit of machine learning. Please don't say it's too hard to understand where or how the magic happens, as there is actually a machine learning for dummies book. It's just different.

On Wed, Jun 30, 2021 at 10:49 AM Philippe Verdy <verdyp@gmail.com> wrote:
You are again making a sever confusion between "lexemes" (your comment is true about them: it is a form in some orthographic system) and "lemmas" (strictly identical to "senses").

I just said that your schema makes 1-to-many relations between LEMMAS and SENSES where this should be 1-to-1.

there are 1-to-many relations from LEXEMES to LEMMAS=SENSES, I've not contested that. but we cannot use LEXEMES as the base of text abstraction (in an abstract language), we'll use LEMMAS.

We don't need any complex relation like LEXEME --(1-to-N)--> LEMMA --(1-to-N)--> SENSE (the second pair is non-sense it should be 1-to-1, and thus merged).

The abstract text will contain LEMMAS (semantic), from which some rules will decide which lexeme (lexical and very specific to each language) to use according to the target language and other constraints, and then which form of the lexeme (grammatical derivations/inflections/conjugation/contextual mutations or particles, plus capitalizing rules for some syntaxic or presentation forms)


Le mer. 30 juin 2021 à 13:18, Andy <borucki.andrzej@gmail.com> a écrit :
Most of most frequent lexems has more than one sense, one sense usually have only rare lexems.
While adding lexem and sense, one must fill not "definition" but "gloss" which should be very short. For example for "dog" is gloss "mammal" although cat and cow are also mammals. It will be good if were both gloss and definition?
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