This document discusses topic modelling and APIs. It proposes representing algorithms like topic modelling as "mills" that encapsulate work without owning data. Mills for topic modelling are described, including resources for creating a topic model, classifying text with a trained model, and getting the classification results. Finally, the current state of machine learning APIs and some references are acknowledged.

1. Topic Modelling
in Farsi
and APIs
@aliostad
Ali Kheyrollahi
Barcelona 2015

2. Machine
Learning
and
APIs

4. Topic Modelling

5. Topic Modelling

6. Topic Modelling
Search

7. Topic Modelling*
Document to document similarity

8. Farsi

9. Farsi
23rd most popular Spoken Language,
above Italian and Polish

10. Farsi
14th most popular Internet Language,
above Korean and Swedish

11. Acquisition and pre-
My Book
‫‌من‬‫ب‬‫کتا‬
ketaab -e- man

12. What did you just
say?
‫‌من‬‫ب‬‫کتا‬
‫ن‬ ‫‌م‬ ‫اب‬ ‫ت‬ ‫ک‬
‫ک‬ ‫ﮐﻢ‬‫ﻳﯾﮑﯽ‬‫ﺗﮏ‬‫ﺑﺎﮎک‬

13. How weird can it get?
Unicode codez

14. And there’s more
‫‌من‬‫ب‬‫کتا‬
‫من‬ ‫کتاب‬
Zero-width non-joiner (0x200C)
‌HTML =>
‫کتابمن‬

15. Topic
Modelling
and
LDA

17. Latent Dirichlet
Allocation (LDA)
✤ Mainly a “clustering” algorithm
✤ Defines topics as latent variables within the documents
✤ Its implementations available in most programming languages
✤ Python => Gensim and Java => Mallet

18. Topic Modelling
concepts in LDA
✤ Document: “Bag of words” vs. “Markov chain”
✤ Word: mere an id (“library”=>123, “librarian”=>789)
✤ Dictionary: set of all words
✤ Corpus: set of all documents
✤ Topic: Distribution over words (LDA)

19. Using Latent Dirichlet
Allocation
✤ Document as a vector of topic weights {0: 0.01, 12: 0.19,
42: 0.23}
✤ Cosine similarity for document similarity
✤ Document similarity works really well
✤ Not great in some domains [to fix => Hierarchical]
✤ Boosting

20. Topic Model
and
APIs

21. Resources: Dictionary
Dictionary
POST languages/farsi/dictionaries HTTP/1.1
Host: example.com
200 OK
Location: languages/farsi/dictionaries/123
languages/{lang}/dictionaries/{id}
Create

22. Resources: Dictionary
Dictionary
PUT languages/farsi/dictionaries/123 HTTP/1.1
Content-Type: application/json
{
“documents”: [
{“fullText”: “‫گفت‬ ‫عروق‬ ‫و‬ ‫قلب‬ ‫‌تخصص‬‫ق‬‫فو‬ ‫,}”یک‬
…
]
}
languages/{lang}/dictionaries/{id}
Add document words

23. Resources: Corpus
Corpus
POST languages/farsi/corpi HTTP/1.1
Host: example.com
200 OK
location: languages/farsi/corpi/123
languages/{lang}/corpora/{id}
Create

24. Resources: Corpus
Corpus
PUT languages/farsi/corpi/123 HTTP/1.1
Content-Type: application/json
{
“documents”: [
{“fullText”: “‫گفت‬ ‫عروق‬ ‫و‬ ‫قلب‬ ‫‌تخصص‬‫ق‬‫فو‬ ‫,}”یک‬
…
]
}
languages/{lang}/corpora/{id}
Add documents

25. Resources: TopicModel
TopicModel
POST languages/farsi/topicmodels?passes=6&alpha=auto HTTP/1.1
Host: example.com
{
“dictionaryId”:123,
“corpusId”:456
}
languages/{lang}/topicmodels
Create (request)

26. Resources*: TopicModel
TopicModel
202 Accepted
Location: languages/farsi/topicmodels/789
languages/{lang}/topicmodels
Create (response)

27. State of current ML APIs

28. State of current ML APIs
HATEOAS
Hypermedia
REST
APIs C A C H E
Markov Chain
Graph Theory
Deep Learning
Bayesian

29. Server Authority
State

30. Server Authority
Algorithm

31. Is this really a resource?
Converts

32. Mills

33. Mills
✤ A single piece of work/specialty (& verb)
✤ Encapsulating an “algorithm”
✤ Do not own data (own config tho):
Raw data in, processed result out
✤ All calls are safe and idempotent

34. Topic Model Mills:
classifier
TopicModellanguages/{lang}/topicmodels/{id}/classifier
classifier (request)
POST languages/farsi/topicmodels/789/classifier HTTP/1.1
Host: example.com
{
“fullText”:“‫گفت‬ ‫عروق‬ ‫و‬ ‫قلب‬ ‫‌تخصص‬‫ق‬‫فو‬ ‫,”یک‬
“refinement”:”hierarchical”
}

35. Topic Model Mills:
classify
TopicModellanguages/{lang}/topicmodels/{id}/classify
classifier (response)
OK 200
Content-Type: application/json
{
“15”: 0.03,
“123”: 0.2,
“390”: 0.09,
…
}

36. Thank you!
@aliostad
aliostad [at] gmail [dot] com

37. Acknowledgements
✤ Windmill picture: https://www.flickr.com/photos/capnkroaker/2473951927/in/photolist-4LBDHz-4Mba53-pnrARE-4Ktk7H
✤ Algorithm picture: https://www.flickr.com/photos/peterrosbjerg/4257452000/in/photolist-7udy9Q-834w2L-dcPgeA-dcPg7s-dcPdHZ-jiRgZs-
jiQstc-8qnJKb-8qdWAF-8qh6D1-8qdWEz-b8ADMZ-b8Ausi-ansdvD-dcPgc9-8kNsd1-pNCgk1-b7G8ZT-8pQRPF-8pTvUy-eXse1A-99XXLF-
eKT1Y-831n9H-jj7vuo-jiRZij-b7G84Z-b7G78P-fvrqUB-b7GajB-jiPF46-8ERYQY-jiNzQv-jiRkkW-jiNUNm-jiPgPZ-jiPbWU-jiNQQm-jiRhDF-jiPF7m-
jiSdJs-jiPqDT-jiSa8u-jiPwr5-jiM5Ac-jiMKze-jiPZCZ-jiNwf6-jiNtMF-jiPHut
✤ Water Tanks picture: https://www.flickr.com/photos/psilver/2280385292/in/photolist-4tvz6N-9h1PHy-cuie7-
RJ83k-696owN-85tcrs-74MqFc-pkuu5-o3BsGV-bR11F2-8jNAnA-ep2fVX-8YyHWv-ABECA-av9mMk-7LMozD-dMySvh-7Pipgo-5rXApy-Q8zgi-
eFxGYc-7sDbjx-87LdLE-aELtQV-7AnXb7-dJqjNR-XYpHK-nAFVCS-95G4EU-9jxNiT-7F1RPj-68hFop-7VFYSs-nzr9W2-pb3zpe-9j5sua-9962cu-
bJ1UED-dp6yqD-8UCQTj-NywAX-kBG3xr-9aTXxq-pVmJui-k8BDsX-7XXtce-7pKUVr-5Hn3CL-rvWcUu-kW6dat
✤ IT Web Jobs: http://www.itjobswatch.co.uk/jobs/uk/machine%20learning.do
✤ Question mark picture: https://www.flickr.com/photos/129627585@N07/15684220620/in/photolist-pTXJmU-4W4Xed-dKRgQ2-
LLBYA-8uuSCh-8qk5Q-4y7wzQ-6feu6Z-6EsuSe-f7eVmb-9WAPNR-f75fPR-8Lzt7R-9L2t4y-apWJQR-fhdGoH-4v7kg9-65wR72-7FyjMW-epmYfa-
abQKN1-6m1HuV-86Uor8-a64uYL-a61DMr-9oCDYc-dW2Xad-a64vnN-a61A6c-2Zvn7-5pjxSz-9Gd43a-oQRf6d-oQReWf-p8kRm6-p8iYSE-
xtEEP-7oxXJg-a64sGS-7U52mA-2Z97S-a61D7e-a64uFd-aiWYZk-2Z9mV-4cmUWW-2Zoxb-2Zg4r-a61Fmi-a61B1T
✤ Timber picture: https://www.flickr.com/photos/simonbleasdale/2797031694/in/photolist-5gaw5o-hLB1BA-9Zafk9-bnjrwy-cSf2fU-
cSf4tN-69qu5j-69qzMC-dkby9F-7wjTFp-kRxidH-53cRdq-nDr93N-kRFANh-25iCW3-cjLSKs-9R81XA-4xHk5Z-9R7MXG-5gay3S-baDJJF-
bnjrdE-9R7XMf-9R8Knj-9R8Ceq-bAej8Z-bnjrhU-8WcymA-
bnjrA5-9R8RgQ-9R5CtZ-9R8e6h-9R8Peh-9R5eca-9R8htw-9R5bbD-9R5yov-9R51kT-9R5hqK-dvvEFb-dvq6fP-dvq7GT-dvvFG7-dvvEyL-dvvGtA-
dvq8bT-dvvGAN-dvq6U2-dvvGWQ-dvq5sD
✤ Timber: https://commons.wikimedia.org/wiki/Category:Timber#/media/File:Oregon_BLM_Forestry_10_(6871708937).jpg

38. References
✤ Gensim: https://radimrehurek.com/gensim/
✤ LDA Paper: http://machinelearning.wustl.edu/mlpapers/
paper_files/BleiNJ03.pdf
✤ Client-Server Domain Separation: http://byterot.blogspot.com.es/
2012/11/client-server-domain-separation-csds-rest.html
✤ Mill proposal: Is coming!