It looks like Opus (the project linked to) goes back to at least 2003, while the coalition behind the Opus Codec chose to create a name collision around 2012.
It would be pretty hard to choose that name without becoming aware of at least one of these. The creators most likely just deemed these other uses too obscure and unrelated to matter, which is fair because it's pretty hard to pick a good name that hasn't already been used somewhere.
It seems they succeeded, besides Wikipedia the first result my Google view has is the codec.
If we have a scarce namespace, first come first served is not a much better way to allocate names than by popularity.
People have yet to successfully stamp a trade mark on words like Opus, and that is perfectly fine by me. Codecs and NLP datasets are distinct enough domains that any confusion will resolve itself quickly.
This is interesting. If I'm understanding right, they collected phrases in parallel from various sources that you can use to feed software that does translation?
Is this (also) a usable source of parallel texts for people doing language learning? (and software that will read it?)
The Opus OpenSubtitles corpus was very useful when I was creating this Chinese-English dictionary app: https://github.com/ReubenBond/HanBaoBao. The tool which creates the dictionary database aggregates several sources, including processing Chinese subtitles for word frequency to inform the most likely cuts when performing word segmentation.
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They intentionally "chose" to create a collision? Or they picked a good name and inadvertently created a collision?
There are dozens of other things called Opus. Just check the Wikipedia page: https://en.wikipedia.org/wiki/Opus
It would be pretty hard to choose that name without becoming aware of at least one of these. The creators most likely just deemed these other uses too obscure and unrelated to matter, which is fair because it's pretty hard to pick a good name that hasn't already been used somewhere.
If we have a scarce namespace, first come first served is not a much better way to allocate names than by popularity.
People have yet to successfully stamp a trade mark on words like Opus, and that is perfectly fine by me. Codecs and NLP datasets are distinct enough domains that any confusion will resolve itself quickly.
Is this (also) a usable source of parallel texts for people doing language learning? (and software that will read it?)