This presentation outlines how BBC News Labs is currently working on entity extraction on BBC News content. It looks at the challenge of how BBC News is going to leverage it's USP of Storytelling, and it's famous purpose - "to INFORM, EDUCATE & ENTERTAIN" globally. With the millions and millions of "things" that are in our content, how do we discover and connect these things?.. It also mentions 2 key BBC News Labs projects "JUICER" and "#newsVANE", and promotes the http://newshack.co.uk/newshack-ii/ event on May 1st in Dublin & Glasgow.

1. Powered by BBC Connected Studio
The Newsroom of Things
ISKO UK “Taming the News Beast”
April 2014
matt shearer – innovation manager
@BBC_News_Labs

2. ABOUT US
“Driving Innovation in News”
NEW TECH AND
DATA
OPPORTUNITIES
NEW
JOURNALISM
FORMATS
EXPLORE
VIA
PROTOTYPING
Part of BBC Connected Studio.
The BBC‟s open innovation programme.

3. WHAT ARE WE* DOING?
* BBC NEWS

4. Focussing on the next 8 yrs.

5. INFORM
EDUCATE
ENTERTAIN

6. DIFFERENTIATOR?

7. Storytelling with
BBC (+) content
past & present
GLOBAL
CURATION

8. HOW?

9. GLOBAL
CURATION
MACHINES DO
HEAVY LIFTING:
Discovery
JOURNALISTS:
Amazing Creative Efficiency.
100% time on curation.
0% time on digging and filing…
MORE MACHINE
WORK:
Connecting and Surfacing
“juicer” “#newsVANE”
Stuff & Things
Trends &
Signals

10. “Stuff & Things”
(and stuff)
The Newsroom of THINGS

11. 30 Languages.
+ 24/7
+ Global
+ Radio + Online + TV
= a lot of things

12. HOW DO WE GET
THE THINGS?

13. THE MACHINES
DISCOVER
THE THINGS

14. It’s notNEW
ROCKET SCIENCE

15. TEXT
…well explored
Things in

16. is a bit more exciting
AV …Things in

17. Stuff & Things (all 6 types = holy grail)
1.Verbatim transcript (to time) “…where she says „damnit!‟”
2.Contributors (face and voice) “who‟s in this segment?”
3.Objects (audio & image recog) “tank or a elephant?”
4.Scene geolocation “this looks like Bangor”
5.Topics mentioned (people, places, orgs,.. Storylines*)
6.Actions & Events (non verbal) “people laughing, kissing”
* Jeremy is telling you in a few mins…

18. from combinations of
this stuff & things
relevance
Then we get real

19. this stuff & things
connecting
and from

20. Since 2012:
R&D JUICER

21. What is it?
• News Content
• Tagged with Linked Data concepts
1
Get
Content
2
Extract
Concepts
3
Match to
DBpedia
4
Annotate
Content
5
Push to
Triplestore
The Juicer
6
Expose
via
API

22. “THINGS”
AT PRESENT:
Topics: PEOPLE, PLACES, ORGANISATIONS
& themes.
NEAR FUTURE:
Storylines: curated “Story” – an editorial THING

23. What’s the content?

24. Unique Things

25. …used how many times?

26. Summary figures
• 680,000 articles tagged with 5,700,000 tags
• ~8 tags per article

27. We save a lot of manual tag time
tags in Juicer 5,700,000
seconds per tag guestimate 10
total seconds on tagging 57,000,000
Mins spent 950,000
Hours 15,833
working days tagging 1,979
working years tagging 9
NB – this is rough, and just for illustration.

28. Next :
Window on the Newsroom
(+AV with transcript generation and speaker recognition)

29. and after that:
GLOBAL
JUICER

30. spaces we are in:
Journalism as Data
MetaJournalism
Object-based Broadcast

31. Come explore with us!
See: newsHACK.co.uk
1st & 2nd May.
#newsHACK II. Glasgow & Dublin
(News Orgs and Academics)

32. @BBC_News_Labs
Thanks
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