A Natural Language Pipeline

Presentation from the spaCy IRL 2019 conference.

1. A Natural Language Pipeline
2. More Input
3. Knowledge” “A compendium of human...
4. Library
5. Physical archives became digital records, encoded with metadata
6. The internet promised rich dynamic experiences
7. The internet promised rich dynamic experiences but served us banner ads
8. Advertising has and continues to fuel a substantial portion of the innovation on the internet
9. What would The Economist look like if it were founded in 2012?
10. User
11. First
12. Experience
13. “There’s a reason that tech companies are topping the lists of most valuable companies and brands. Every company is a tech company.” Maggie Chan Jones
14. Every story, at its core, is a business story
15. Language
18. Stage -> Stenographer -> Editors -> spaCy -> Data Store <-> Backend <- Slack <- Users Proto-Pipeline
19. Over eight hours we created data from the content of the event, building the model in real-time
20. The model evolved over time
21. This was the experiment that would evolve into SiO 2
22. Silicon, a key element in everything from glass to microchips, is at the core of global business
23. Oxygen, the journalistic voice Quartz breathes into the global business news cycle
24. Entities are linguistic anchors, defined by context and around which context can be inferred
25. Standard Entities PERSON FACILITY ORG PRODUCT GPE EVENT... Additional Entities TECHNOLOGY PROCESS NATURE MEDIA CONSTRUCT
26. 70K articles 1.4M blocks of text 85K labeled sentences
27. Entities
28. This spaCy model made rich analysis for any given text easy to do on the fly
29. Stored analysis of a large corpus is a vital resource
30. The language graph...
31. Graph
32. The language graph is a mutable map of the language model
33. Any new content is analyzed and then mapped onto the language graph
34. Changes made to the graph can then be incorporated into the next model iteration
35. The language graph becomes a primary resource for extracting training data
36. Snapshots of time can be extracted from the language graph
37. Context can be derived by looking at the relationships in the language graph
38. Elon Musk
39. Jeff Bezos
40. Mark Zuckerberg
41. Context
42. SiO 2 is a living Natural Language Pipeline of networked algorithms trained on the corpus of Quartz to understand the linguistic patterns of global business news
43. The Pipeline(s) Quartz Corpus -> Training Sentences -> spaCy Content -> spaCy -> Language Graph Language Graph -> Training Data -> Statistical Models / Classifiers Language Graph -> Training Sentences -> spaCy Unseen Content -> spaCy -> Pre-Processed Text / Vectors -> Statistical Models / Classifiers
44. Thank you