information extraction, modelling and storage of semantic data to recognize trending topics for journalism and newspaper offices

1. Michael Aleythe, Martin Voigt, Peter Wehner
Sächsische AufbauBank
Forschung und Entwicklung - Projektförderung
Projektnummer - 99457/2677

2. Structure
Motivation, Problems, and Goals
Topic/S Workflow
Demo
Conclusion
Friday, 06.09.2013
Topic/S
Slide 1

3. Motivation
Newsroom
Quelle: ringier.com
Friday, 06.09.2013
Topic/S
Slide 2

4. Problem
In-house production
Archive
News agencies
Web, social media
Online
DPA
Twitter
Reuters
Facebook
KNA
Blogs
…
…
Overwhelming amount of data
e.g., WAZ  5000 articles/day from agencies and inhouse production
Friday, 06.09.2013
Topic/S
Slide 3

5. Vision
Automatic topic discovery using Named Entities and
other keywords (Semantic Items, SemItem)
Investigation of trending topics
Media
Assets
Named
Entities
Topics
E1
E2
E3
MA1
E4
Push them to the editor
T1
T2
E5
MA2
E6
T3
E7
Pre-Processing
Friday, 06.09.2013
Topic/S
Slide 4
Post-Processing

6. Structure
Motivation, Problems, and Goals
Topic/S Workflow
–
–
–
–
Overview
Information Extraction
Storage
Topic Detection
Demo
Conclusion
Friday, 06.09.2013
Topic/S
Slide 5

7. Workflow
Friday, 06.09.2013
Topic/S
Slide 6

8. Workflow: Preprocessor
Language Recognition (Ger/Eng)
Rule based
Named Entity Extraction
word list + statistics
Source: onelanguageoneposter.com
Keyword Extraction
Lemmatization, word list
Categorisation
Source based
Friday, 06.09.2013
Topic/S
Slide 7

9. Structure
Motivation, Problems, and Goals
Topic/S Workflow
–
–
–
–
Overview
Information Extraction
Storage
Topic Detection
Demo
Conclusion
Friday, 06.09.2013
Topic/S
Slide 8

10. Semantic Model
Friday, 06.09.2013
Topic/S
Slide 9

11. Semantic Facts
Named Entities required but no lists available
SemItem
Number (with alt. names)
Person
1.504.341 (2.499.962)
Organization
63.332 (98.127)
Place
89.702 (95.178)
Keyword
1351
Stored preferred and alternative names
ID: http://www.topic-s.de/topics-facts/id/person/Rene_Muller
Names: Rene Muller, Rene Müller, René Muller, René Müller
Triples without SemItems: 27,6 Mio.
Friday, 06.09.2013
Topic/S
Slide 10

12. Storage of Semantic Data
Using Oracle 11gR2
Pros
Already available, existing knowledge
Integrated querying of relational and
semantic data
Cons
Inference
Incomplete SPARQL 1.1 support
Limited custom rule support
Benchmark of triple stores [Voigt2012]
Friday, 06.09.2013
Topic/S
Slide 11

13. Structure
Motivation, Problems, and Goals
Topic/S Workflow
–
–
–
–
Overview
Information Extraction
Storage
Topic Detection
Demo
Conclusion
Friday, 06.09.2013
Topic/S
Slide 12

14. Workflow: Topic Detection
Clustering
Friday, 06.09.2013
Topic/S
Slide 13

15. Workflow: Topic Detection
Clustering
Friday, 06.09.2013
Topic/S
Slide 14

16. Workflow: Topic Detection
Clustering
Obama
Merkel
Politics
Audi
Highway
Traffic
Friday, 06.09.2013
Topic/S
Slide 15

17. Workflow: Topic Detection
Clustering (Top Cluster 25.08.2013)
Article Name
43
Bundesliga, Fußball, Spieltag , 1. FC Union Berlin,
SC Paderborn 07 eV, FC Augsburg, FSV Frankfurt
Yes
25
Euro, SPD, Berlin, Griechenland, FDP, CDU,
Deutschland
Yes
19
Bericht, Diplomat, Google Inc , Anbieter, Berlin,
Deutschland, Auto
Yes
18
Veranstaltung, Bernd Lucke, Angreifer, Berlin,
Polizei, Angriff, Deutschland
Yes
15
Friday, 06.09.2013
HotTopic
Gericht, Prozess, Bo Xilai, Christian Wulff,
Anklage, Verfahren, Mord
Yes
Topic/S
Slide 16

18. Structure
Motivation, Problems, and Goals
Topic/S Workflow
Demo
Conclusion
Friday, 06.09.2013
Topic/S
Slide 17

19. Live Demo
Friday, 06.09.2013
Topic/S
Slide 18

20. Structure
Motivation, Problems, and Goals
Topic/S Workflow
Demo
Conclusion
Friday, 06.09.2013
Topic/S
Slide 19

21. Sum it up!
Result
Identifying topics and pushing them
to the editor
Lessons learned
NER: bad for non-English,
combination required
model needs to be optimized for
queries
dedicated user interface required
Outlook
prediction of topics with
causal/temporal relations
Friday, 06.09.2013
Topic/S
Quelle: ooltapulta.com
Quelle: business-strategy-innovation.com
Slide 20

22. Thanks! Questions?
Sächsische AufbauBank
Forschung und Entwicklung - Projektförderung
Projektnummer - 99457/2677

23. Workflow: Preprocessor
Named Entity Recognition
word list
Tool: LingPipe + Extension
Quelle: churchthought.com
Sources: LOD (DBPedia, Geonames, YAGO2, GND)
Advantages: controlled vocabulary, guarantied
recognition of entities
statistics
Tool: Stanford NLP
Source: pre-trained model
Advantage: Recognition of unknown entities
Friday, 06.09.2013
Topic/S
Slide 22

24. Workflow: Preprocessor
Categorization
Categoriser Reuters
Politics
Article DPA
Categoriser DPA
IPTC Media Topic
Categoriser OTS
Friday, 06.09.2013
Topic/S
Slide 23

25. Workflow: Preprocessor
Categorization - Quality
News-Agency
KNA
80,3 %
DPA
94,4 %
EPD
80,3 %
Reuters
90,8 %
OTS
93,5 %
AFP
86 %
Method
accuracy
One cat. for all agencies
85 %
One cat. per agency
Friday, 06.09.2013
accuracy
87,5 %
Topic/S
Slide 24

26. Workflow: Preprocessor
Keywords
Lemmatization
Quelle: hugdaily.org
Developing a word list
Extraction using the word list
Bonus: frequent terms of an article
Friday, 06.09.2013
Topic/S
Slide 25

27. Disambiguation
Quelle: de.wikipedia.org
Quelle: fansshare.com
Quelle: lounge.espdisk.com
Friday, 06.09.2013
Topic/S
Slide 26

28. Disambiguation
Identification of
Entity Cluster
Michael Jackson
Internal Facts
Beer
Michael Jackson
Beer
Whiskey
Michael Jackson
External Facts
(DBpedia, etc.)
Music
King of Pop
Problem: not all SemItems available in the LOD
Friday, 06.09.2013
Topic/S
Slide 27