The goal is to have a taxonomy that more closely maps to newcomers' expectations.
We want to take the taxonomy from T240276: Restructure WikiProject directory to be better and apply to the current pipeline in https://github.com/wikimedia/drafttopic rather than using the old taxonomy that was extracted from Wikipedia.
This task is done when: The new topic model is deployed @ https://ores.wikimedia.org
| Status | Subtype | Assigned | Task | ||
|---|---|---|---|---|---|
| Resolved | Halfak | T243451 Deploy ORES -- Late Jan 2020 | |||
| Resolved | Halfak | T235181 Build WikiProject directory topic models for ar, cs, and kowiki | |||
| Resolved | Halfak | T235183 Experiment with different vector lengths for ar, cs, en, and kowiki topic models. | |||
| Resolved | Halfak | T235187 Create labeled data for topic models in ar, cs, kowiki | |||
| Resolved | • Rileych | T240517 [EPIC] Growth: Newcomer tasks 1.1.1 (ORES topics) | |||
| Resolved | Isaac | T236713 Improve drafttopic training data pipeline | |||
| Resolved | Halfak | T242035 First deployment of the new decade! | |||
| Resolved | Halfak | T240286 Re-train English Wikipedia topic model using new WikiProject Taxonomy | |||
| Resolved | Halfak | T240276 Restructure WikiProject directory to be better | |||
| Resolved | kevinbazira | T240282 Improve WikiProject template --> WikiProject mapping |
Using our old vectors, it looks like we're getting decent fitness. I've trained models on article text (most recent revision) and draft text (first revision) and I'm seeing similar fitness. There are a few topics that don't get good fitness.
The worst topics in the article topic model (PR-AUC):
Geography.Regions.Africa.Central Africa 0 Culture.Media.Software 0.108 STEM.Mathematics 0.123 Geography.Regions.Asia.North Asia 0.189 STEM.Physics 0.204 STEM.Computing 0.214 History and Society.Society 0.227 History and Society.History 0.229 STEM.Technology 0.251 Geography.Regions.Africa.Northern Africa 0.262 Geography.Regions.Africa.Eastern Africa 0.265 Culture.Media.Entertainment 0.28
The word topics in the draft topic mode(PR-AUC):
Geography.Regions.Africa.Central Africa 0 Culture.Media.Software 0.103 STEM.Physics 0.195 STEM.Computing 0.203 Geography.Regions.Asia.North Asia 0.206 STEM.Mathematics 0.218 History and Society.Society 0.229 STEM.Technology 0.236 Geography.Regions.Africa.Eastern Africa 0.237 History and Society.History 0.25 STEM.Libraries & Information 0.269 Culture.Media.Entertainment 0.278 Geography.Regions.Africa.Northern Africa 0.296
There's a lot of overlap here. So I don't think it's a lack of text content that accounts for the badness of these cases. Let's see what we're training on.
$ bzcat datasets/enwiki.labeled_article_items.json.bz2 | grep "Geography.Regions.Africa.Central Africa" | json2tsv sitelinks.en | shuf | head Marc Mbombo Likati District Democratic Republic of the Congo–Holy See relations Rund Kanika Red Cross of the Democratic Republic of the Congo Bruno Tshibala Tshibalabala Kadima Kinshasa Democratic Republic of the Congo Temple Lever Brothers Ikoko-Impenge
It looks like these are consistent, but we only have 18 observations total! It looks like the relevant WikiProjects aren't getting picked up.
We have the following WikiProjects. All of them are sub-projects of WikiProject Africa and do not use their own template. The rare exception is the small set of articles that use {{WikiProject Democratic Republic of the Congo}} which is a redirect to the {{WikiProject Africa}} template.
- WikiProject Angola - WikiProject Cameroon - WikiProject Central African Republic - WikiProject Chad - WikiProject Democratic Republic of the Congo - WikiProject Equatorial Guinea - WikiProject Republic of the Congo - WikiProject Gabon - WikiProject Sao Tome and Principe
Random sample of articles:
HTML Microsoft Windows version history Editor war AOL GNU C++ Common Language Infrastructure Grace Hopper Fortran GIF
At a glance, these all seem relevant to software except maybe "Editor war". Maybe this category is just too wide.
Random sample of articles:
Rock paper scissors Cryptography Mathematical game OpenSSH List of algorithms Probability distribution Pretty Good Privacy Elliptic-curve cryptography Normal distribution Integer factorization
This looks pretty good. There's a lot of probability math and algorithms stuff in there. OpenSSH and PGP don't seem like a real "math" articles, but I would expect they get pulled in through WikiProject Cryptography. I wonder if we should exclude that WikiProject. Anyway, it's weird that we aren't really learning what "Mathematics" includes as this does seem to include a consistent set.
Random sample of tagged articles:
North Ossetia–Alania Naukograd Adygea NKVD Boris Delibash Lapland War T-34 Grigory Zinoviev SVT-40
It looks like there's a varied set of things that WikiProject Russia cares about that are not geographical in nature. E.g., the SVT-40 is a gun and Boris Delibash is a person.
Steven Weinberg Inertial frame of reference Chung-Yao Chao Miscibility Age of the Earth Figure of the Earth Sachs–Wolfe effect Theory of relativity Euclidean vector Jens Martin Knudsen
Well, "Age of the Earth" is tagged by just about every WikiProject.
I wonder if there is something to do with Jargon being missing. I dug into the vectors we have and learned a few things.
#3 is really bad because it means that articles that have words that aren't in the vectors get a lot of zero vectors averaged in. We should probably just not emit a vector if we can't look one up for a word!
I solved the zero vectors issue and retrained the models. Here's the difference:
Old New Diff Geography.Regions.Africa.Central Africa 0 0 0 Culture.Media.Software 0.108 0.145 ++ STEM.Mathematics 0.123 0.161 ++ Geography.Regions.Asia.North Asia 0.189 0.18 - STEM.Computing 0.214 0.19 -- STEM.Physics 0.204 0.232 ++ History and Society.Society 0.227 0.233 + History and Society.History 0.229 0.237 + Geography.Regions.Africa.Eastern Africa 0.265 0.247 -- STEM.Technology 0.251 0.248 - Culture.Media.Entertainment 0.28 0.26 -- Geography.Regions.Africa.Northern Africa 0.262 0.267 +
It looks like we didn't really see any meaningful benefit. Bummer. I still have hope though that we'll be able to get better signal from the fasttext vectors that @kevinbazira is working on because they'll have vectors for deep jargon like "redshift" and "flourine".
We'll likely be able to deal with some of the geographic misses by pulling in geotag info.
I did some digging into the 50 cell models that Kevin trained. It looks like we do pick up "redshift" in the first 300k words:
kv.similar_by_word('redshift')
[('redshifts', 0.967665433883667), ('redshifted', 0.9275557994842529), ('quasars', 0.9235901832580566), ('microlensing', 0.916486918926239), ('grbs', 0.9055891036987305), ('lensing', 0.8869045972824097), ('pulsars', 0.8805599808692932), ('gravitational', 0.8784142732620239), ('extrasolar', 0.8742184042930603), ('photometry', 0.873486340045929)]But it doesn't look like we pick up "flourine"
>>> kv.similar_by_word('flourine')
<snip>
KeyError: "word 'flourine' not in vocabulary"I couldn't even get "flourine" with 500k words but I could get it if I loaded in the *entire model*.
>>> kv.similar_by_word('flourine')
[('crusts', 0.8145809769630432), ('triturated', 0.8126140832901001), ('carbonating', 0.8122193813323975), ('glaze', 0.8105583190917969), ('remineralized', 0.805204451084137), ('kaolin', 0.8033907413482666), ('flouring', 0.8021371960639954), ('bleaches', 0.7986146211624146), ('mordanted', 0.7982538342475891), ('macerated', 0.7974271774291992)]This looks like an alright set of words.
Let's try the 100 cell models!
>>> kv.similar_by_word('redshift')
[('redshifts', 0.9580318927764893), ('redshifted', 0.9244788289070129), ('quasars', 0.8797826766967773), ('microlensing', 0.856326699256897), ('parsecs', 0.8424521684646606), ('kiloparsecs', 0.8395386934280396), ('grbs', 0.8378009796142578), ('pulsars', 0.8366929292678833), ('gravitational', 0.8292627930641174), ('bolometric', 0.8287577033042908)]That's a pretty similar result. Let's try something a bit harder.
>>> kv.similar_by_word('permutations')
[('permutation', 0.959696352481842), ('permuting', 0.9091914892196655), ('integers', 0.8868318796157837), ('multiplications', 0.8791753053665161), ('multisets', 0.8739590048789978), ('vertices', 0.8692960739135742), ('permute', 0.8651570081710815), ('multiplicities', 0.8635972738265991), ('automorphisms', 0.8589743375778198), ('tuples', 0.8580431938171387)]
>>> kv.similar_by_word('combinatorics')
[('combinatorial', 0.9255411624908447), ('algebraic', 0.8925043344497681), ('mathematical', 0.8895950317382812), ('mathematic', 0.8747307062149048), ('computability', 0.8731629848480225), ('theorems', 0.8695327043533325), ('combinatory', 0.8691357374191284), ('theoretic', 0.8615963459014893), ('diophantine', 0.8591476678848267), ('metamathematics', 0.8570147752761841)]Looks like we're getting good signal there.
I wonder how our old vectors performed.
>>> kv.similar_by_word('redshift')
KeyError: "word 'redshift' not in vocabulary"
>>> kv.similar_by_word('factorial')
KeyError: "word 'factorial' not in vocabulary"
>>> kv.similar_by_word('permutations')
[('combinations', 0.5920488238334656), ('variables', 0.4811089038848877), ('possibilities', 0.47414517402648926), ('variations', 0.4657440185546875), ('scenarios', 0.46018773317337036), ('equations', 0.43501317501068115), ('twists', 0.4297747313976288), ('guises', 0.427018404006958), ('paradoxes', 0.4257811903953552), ('ifs', 0.41084015369415283)]
>>> kv.similar_by_word('combinatorics')
KeyError: "word 'combinatorics' not in vocabulary"Aha! It looks like our current model is pretty bad. That's good news. Seems like we can get a lot of signal from the new vectors.
Regretfully, I was not able to get better signal from the new vectors. I'm not quite sure why. For this work, I looked at the aggregate PR_AUC measure, but the fitness for the topics I reference above correlates pretty strongly with the overal PR_AUC averages.
Generally, the word2vec-based vectors from google news perform better than fasttext. I tried doing some hyperparameter optimization and it looks like the model gets better fitness with more estimators and more depth, but the model gets huge. See the note for the "wikipedia 50 cell skipgram".
So, my plan for now is to deploy the gnews 300 cell model ASAP and continue explorations here longer-term.