Topic/S – A Topic and Trend Recognition Approach in News-Media, I-Semantics13

Sächsische AufbauBank Forschung und Entwicklung - Projektförderung Projektnummer - 99457/2677

Michael Aleythe, Martin Voigt, Peter Wehner

Structure

Motivation, Problems, and Goals

Topic/S Workflow

Demo

Conclusion

Friday, 06.09.2013 Topic/S Slide 1

Motivation

Newsroom


Quelle: ringier.com

Problem

Overwhelming amount of data

e.g., WAZ 5000 articles/day from agencies and in-house production

Friday, 06.09.2013 Topic/S

DPA

Reuters

KNA

Twitter

Facebook

Blogs

…

News agencies Web, social media

…

In-house production

Archive

Online

Slide 3

Vision

Automatic topic discovery using Named Entities and other keywords (Semantic Items, SemItem)

Investigation of trending topics

Push them to the editor


MA1

E1

E2

E4

E3

E7

E6

E5MA2

Media Assets

Named Entities

Pre-Processing

MA1

E1

T1E2

E4

E3

E7

E6

T2

T3

E5MA2

Media Assets

Named Entities

Topics

Pre-Processing Post-Processing

Slide 4

Structure


Topic/S Workflow

– Overview

– Information Extraction

– Storage

– Topic Detection

Demo

Conclusion


Workflow


Workflow: Preprocessor


Language Recognition (Ger/Eng)

Rule based

Named Entity Extraction

word list + statistics

Keyword Extraction

Lemmatization, word list

Categorisation

Source based

Slide 7

Source: onelanguageoneposter.com

Structure


Topic/S Workflow

– Overview


– Storage

– Topic Detection

Demo

Conclusion


Semantic Model


Semantic Facts

Named Entities required but no lists available

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.


SemItem Number (with alt. names)

Person 1.504.341 (2.499.962)

Organization 63.332 (98.127)

Place 89.702 (95.178)

Keyword 1351

http://www.topic-s.de/topics-facts/id/person/Rene_Muller







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]


Structure


Topic/S Workflow

– Overview


– Storage

– Topic Detection

Demo

Conclusion


Workflow: Topic Detection


Clustering

Slide 13



Clustering

Slide 14



Clustering

Merkel

Politics

Highway

Traffic

Audi

Obama

Slide 15



Clustering (Top Cluster 25.08.2013)

Article Name HotTopic

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 Gericht, Prozess, Bo Xilai, Christian Wulff, Anklage, Verfahren, Mord

Yes

Slide 16

Structure


Topic/S Workflow

Demo

Conclusion


Live Demo


Structure


Topic/S Workflow

Demo

Conclusion


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


Quelle: ooltapulta.com

Quelle: business-strategy-innovation.com

Sächsische AufbauBank Forschung und Entwicklung - Projektförderung Projektnummer - 99457/2677

Thanks! Questions?



Named Entity Recognition

word list

Tool: LingPipe + Extension

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

Slide 22

Quelle: churchthought.com



Categorization

Politics

Article DPA IPTC Media Topic

Categoriser OTS

Categoriser DPA

Categoriser Reuters

Slide 23



Categorization - Quality

News-Agency accuracy

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 87,5 %

Slide 24



Keywords

Lemmatization

Developing a word list

Extraction using the word list

Bonus: frequent terms of an article

Slide 25

Quelle: hugdaily.org

Disambiguation


Quelle: fansshare.com Quelle: lounge.espdisk.com

Quelle: de.wikipedia.org

Disambiguation

Problem: not all SemItems available in the LOD


Michael Jackson

Beer

Michael Jackson

Beer

Whiskey

Michael Jackson

Music

King of Pop

Internal Facts

External Facts (DBpedia, etc.)

Identification of Entity Cluster

Slide 27

Topic/S – A Topic and Trend Recognition Approach in News-Media, I-Semantics13

Technology

Transcript of Topic/S – A Topic and Trend Recognition Approach in News-Media, I-Semantics13