Le 30/07/2015 16:50, Trey Jones a écrit :
Thanks for all the technical details! So much going on... so much to learn!

I didn't know/remember that suggester only works on titles and redirects. Then, obviously, using just that would be great! That's gotta be a 98%+ reduction in text.

Yes and Erik suggested that we could try to inject more content.
This option already exists and we could turn it on, but I suspect it was disabled in wmf config for good reasons.

I like your reasonable process—it's quite reasonable!

You asked about which wikis to look at. Are en, fr, de, it and es the ones we can best read? (I'm okay with that to start, by the way—it optimizes developer time.) By number of zero-result queries from my 500K sample, the top five are en, de, pt, ja, and ru—though that sample is small. By overall size, it's en, sv, de, nl, and fr. Clearly enwiki dominates, and I'm guessing the performance will differ across languages—so I don't have a clear suggestion here. But enwiki makes sense because it's the biggest on every front, and itwiki, because it does the most interesting crosswiki stuff.

Hmm. Is enwiki big enough to drag everything else along if it's very beneficial there?

If we have a process that works for enwiki it'd be "easy" to reiterate over other wikis. I'd say we could start with enwiki.

    We have some technical restrictions here, if we activate this
    settings on one wiki we'll need to reindex most of the wikis
    because we have cross-wiki searches.

    wikiA can query wikiB's index, if wikiB index is not updated with
    correct settings the query will fail.


    So it's hard to work with mixed settings with the current
    architecture :(

I'm a bit confused. Will elasticsearch do really bad things if you ask it to search in a way that isn't enabled on a particular index? Does fail mean zero results, or does it waste lots of CPU and start throwing errors? Is there a reasonable way to assess what features a query needs and whether a given index supports those features? Sounds terribly ugly, but I had to ask.

"Fails" means a big red message displayed to the user :)
Elasticsearch can run a single query over multiple indexes. In the case you ask for a suggest field that's missing in one of the index you requested the whole query will fail. Today we have a config per wiki and not a config per index, having a config per index would imply a big refactoring and we would have to drop this convenient "multi-index" feature.

    Note that we will not be able to measure things like :
    search is a better than samech for the query saerch.

    This seems impossible to check without human review. We could do
    another run with queries where a suggestion was found and generate
a diff that will be reviewed by hand:

    user_query: saerch
    prod_suggestion: samech
    with_reverse: search

Are you thinking of manual review of the suggestions, or of a diff of the results of the suggestions? I'm assuming just looking at the terms—I feel that a fluent speaker could easily tell that search is better than samech just by looking at the words. (So I could help review in English, at least.)

Yes the idea was to extract only the suggestions that differ from the one we have in search logs.

That said, there are two things I can think of that would make for at least a weak heuristic: edit distance and frequency.

Since there only going to be a small number of suggestions in each case, running full edit distance on them offline wouldn't be too costly. There are many versions of edit distance you could use. With plain dumb E.D., these are both distance 2, but with reversals counting less than a full insert + delete, "search" is better than "samech". You can also do more generic weighted edit distance to allow typos (x is more likely for z than p for z) or likely spelling errors (mixing up vowels or double vs single letters) to count less.

As for frequency, you could look at overall term frequency or document frequency in the index, or if that's too expensive, get a generic frequency list for the language in question. "search" is clearly better than "samech" by any frequency metric.

With an index in lab I can extract the frequencies, you'll have something like :

search engine:122
google search:32
google search engine:2

You will have to filter on space to keep only unigrams if it's better for you.

We could take hand-reviewed results (seems like it'd be quick work—I'd do a pile from enwiki) as training data to fit a model that would allow us to predict which suggestions are likely to be better.

If/when we do roll it out to production, we could obviously further test by giving multiple suggestions and seeing which ones users like.

This is another very good idea :)
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