Topic on User talk:TJones (WMF)/Notes/Potential Applications of Natural Language Processing to On-Wiki Search

Word embedding models, like en:fastText and en:GloVe, can be used to generate synonyms and similar words. When trained on text w/ many misspellings, they can also work to suggest spelling fixes.

Facebook's FAIR lab produced pre-trained models on 294 languages of Wikipedia (link). These being trained on Wikipedia text, won't be very good at spelling mistakes as the source material is very clean. It would be very interesting to train a fastText model on your query logs and see what is produced by the nearest neighbor search in the word embedding space.

You can explore the nearest neighbor search by grabbing one of the models and running fastText:

./fasttext nn en.wiki.bin 50

Searching for "imbedding", the closest 50 words in the 300-dimensional word embedding space are:

Query word? imbedding

1. imbeddings 0.941056
2. embedding 0.880808
3. embeddings 0.875705
4. compactification 0.732301
5. diffeomorphism 0.729409
6. compactifying 0.729186
7. antihomomorphism 0.726086
8. compactifications 0.724407
9. geometrization 0.721966
10. biholomorphism 0.721854
11. isomorphism 0.721106
12. homeomorphism 0.719762
13. homotopic 0.717359
14. parametrization 0.717293
15. parametrizations 0.716476
16. injective 0.715966
17. diffeomorphisms 0.715271
18. automorphism 0.714177
19. biholomorphisms 0.71407
20. submanifold 0.711693
21. antiholomorphic 0.711509
22. topological 0.711504
23. geometrizable 0.710431
24. automorphisms 0.708235
25. homeomorphisms 0.708069
26. codimension 0.706777
27. projective 0.7067
28. generalizes 0.706284
29. endomorphism 0.705661
30. simplicial 0.705504
31. reparametrizations 0.7055
32. hypersurface 0.705288
33. parametrizing 0.704711
34. codimensional 0.704644
35. reparametrization 0.703381
36. quasitopological 0.703158
37. nullhomotopic 0.703086
38. quasiconformal 0.703035
39. hypersurfaces 0.700519
40. biholomorphic 0.69997
41. antiautomorphism 0.699786
42. geometrizes 0.699575
43. submanifolds 0.699203
44. compactified 0.69918
45. conformal 0.699034
46. embeddability 0.69899
47. pseudoholomorphic 0.698393
48. complexification 0.698191
49. holomorphicity 0.698155
50. nonsingularity 0.697529

Sorry if I am asking something very obvious, but I understand that the models are based on the word co-occurrence. For a text, this makes a lot of sense, but queries are usually very short and frequently omit words. Would we have enough data in the query corpus to have good word relationships?

Also, fastText seems to split words into n-grams, which should work ok with misspellings (at least ones that do not make the word completely unrecognizable).