This is also on Phab for longer-term documentation, but I'm copying it here for wider distribution.
TextCat and Language Detection
Back before the holidays (12/23/2015), Stas and Trey had a conversation on IRC about TextCat and Lang ID. There was lots of good stuff in the conversation, so the main points are summarized here, to record for posterity, and to open them up to further conversation if anyone has any additional ideas.
For reference, the main Phab ticket for language ID stuff is T118278: EPIC: Improve Language Identification for use in Cirrus Search
Building Language Models: It seems like we should try to create language models to cover at least the same set of languages as the original TextCat. The original models were in various encodings, but we’d create (and have created) models in Unicode. In general, we saw better performance doing language detection on queries using models built on queries. If we want to support general language identification, we could also build models based on text from Wikipedia (which we need to do for some languages anyway because the query data is so poor). It’s a relatively straightforward task, compared to getting sufficiently high quality query data.
Using Language Models: We get the biggest improvement in language detection accuracy (~20% increase in F0.5) from restricting the list of candidate languages based on their individual performance and the distribution of languages we encounter in real life, rather than using all available languages. We need our new TextCat to support the ability to specify which models to use. It makes sense to create models based on both query data (if we have it) and general text (from Wikipedia) and make them available, probably through Stas’s PHP version of TextCat on GitHub. Trey will also be putting the Perl version and language models up on GitHub after a bit more cleanup.
Choosing Language Models: In order to choose which models to use on a particular wiki, we need to sample queries and manually identify the languages represented, and then experimentally determine the best set of language models to use. We will do this for the wikis with the highest query volume, and see how far down the list we have time to work on. For any wikis we don’t get to, we can try using a generic set of languages, or just not do language detection for now, or make general capabilities available as an opt-in feature—though we need to think more carefully about how to handle smaller wikis, especially after we have more experience using TextCat on larger wikis.
In addition to evaluation sets for particular wikis, we’re have a task to create a “balanced” set of queries in known languages for top wikis (by query volume) for general evaluation of language models, which can help us determine a generic set of more-or-less reliable languages. (These are smaller sets that let us gauge general performance, but not enough for training language models.)
Updating Language Model Choices: Trey’s estimate/intuition (which could use some validation) is that the per-wiki language lists would need updating at most once a quarter, though it’s possible that with appropriate metrics we could determine that we needed to do an update by a sudden or sustained gradual decrease in performance. We may need to think this through a bit more carefully, since different update pattern imply different places/ways to store the list of relevant language models. Stas says that quarterly updates are close enough to static to put language lists into some file in the Cirrus source, pretty much like we do with indexing profiles, etc. Alternatively, if updates are more frequent and per-wiki, we could store the list of languages to use in mediawiki-config.
 https://phabricator.wikimedia.org/T121547, etc. See  for more.
Software Engineer, Discovery
On Tue, Dec 22, 2015 at 1:27 PM, Stas Malyshev <firstname.lastname@example.org> wrote: