MeaningCloud participatation in T3chFest 2019, the technology fair of the University Carlos III of Madrid. In “NLP for Small Data“ we review the state of the art in the Natural Language Processing. We also discuss the advances in Deep Learning and the usage of Linguistic Models. More info at https://www.meaningcloud.com/

1. NLP for Small Data
Concepción Polo
María José García

2. NLP is out there

4. NLP is complicated

5. He comprado libros y muebles de segunda mano
El perro de Juan
Aceite de oliva, aceite de palma… aceite de bebé

6. En casa nos gusta comer cocido

8. Phonetics and Phonology
Natural Language

9. Morphology and Lexicon
Phonetics and Phonology
Natural Language

10. Syntax
Morphology and Lexicon
Phonetics and Phonology
Natural Language

11. “Me gusta mucho el cuadro de Juan”
¿Juan posee el cuadro?
¿Juan ha pintado el cuadro?
¿Juan está pintado en el cuadro?

12. Semantics
Syntax
Morphology and Lexicon
Phonetics and Phonology
Natural Language

13. Juan es / está gordo
Christian Bale #es / está gordo
Paquita Salas es / #está gorda

14. Pragmatics
Semantics
Syntax
Morphology and Lexicon
Phonetics and Phonology
Natural Language

15. time
big bang
Deep Learning
we are here
Alan Turing
people drawing strange
pictures of eyes and birds
in Egypt
EMACS doctor
ML
?

16. time
big bang
Deep Learning
we are here
Alan Turing
people drawing strange
pictures of eyes and birds
in Egypt
EMACS doctor
ML
?

17. Rationalism

18. (part of NLTK)

20. {
(([{ner:PERSON}]) /was/ /born/ /on/ ([{ner:DATE}]))
=> "DATE_OF_BIRTH"
}
(https://nlp.stanford.edu/software/tokensregex.html)

21. Languages evolve!

22. time
big bang
Deep Learning
we are here
Alan Turing
people drawing strange
pictures of eyes and birds
in Egypt
EMACS doctor
ML
?

23. Empiricism

24. Features

25. hello
mom
dad
bye
Hello, mom!
Hello, dad!
Bye!
1
2
3
4
[1, 1, 0, 0]
[1, 0, 1, 0]
[0, 0, 0, 1]

26. A good starting point but...

27. time
big bang
Deep Learning
we are here
Alan Turing
people drawing strange
pictures of eyes and birds
in Egypt
EMACS doctor
ML
?

28. Deep Learning

30. Embeddings
Word2Vec
fastText
GloVe
Good
Bad
Bright
Dark
White
Black

31. http://jalammar.github.io/illustrated-bert/

32. Needs a lot of data

33. Case of study: big telco

35. 15k summaries
16 words average
typos, abbreviations
250+ root causes
10 days

36. Señor,
llévame
pronto.

37. Deep
Learning

38. Deep
Learning
Linguistics

39. Text
DL Model
Linguistic model

40. “We are observing the death of a star”
Astronomy
OK astronomy

41. “Planets move in circles inside other circles”
Astronomy
More like ancient astronomy

42. “I am building a Death Star”
Astronomy
LOL
no

43. Accuracy
Time

44. Deep
Learning
Linguistics

45. time
big bang
Deep Learning
we are here
Alan Turing
people drawing strange
pictures of eyes and birds
in Egypt
EMACS doctor
ML
?

46. Transfer
Learning
Linguistics

47. Transfer
Learning
17h
Track 4

48. Thank you!
cpolo@meaningcloud.com
mgarcia@meaningcloud.com
New York
3537 36th St
+1 (646) 403-3104
Madrid
Labastida, 1
+34 910 754 276
www.meaningcloud.com
No engineer was hurt in the making of this presentation