Text Mining: Creating Semantics in the Real World

Transcrição

Prof. Dr. Stefan Wrobel
Dr. Gerd Paass, Andreas Schäfer, Dr. Stefan Eickeler
Fraunhofer IAIS: Intelligent Analysis and Information Systems
250 people: scientists, project engineers,
technical and administrative staff, students
Located on Fraunhofer Campus Schloss
Birlinghoven/Bonn
Joint research groups and cooperation with
Core research areas:
Machine learning/data mining
Multimedia pattern recognition
Visual Analytics
Process Intelligence
Adaptive robotics
Cooperating objects
Directors: T. Christaller, S. Wrobel (exec.)
2
Brainyquote.com
Where is all the knowledge we lost with information?
T. S. Eliot
Thomas Stearns Eliot, OM (September 26, 1888 – January 4, 1965)
US-born British poet, dramatist and literary critic
3
Outline
Why is Text Mining cool?
• Drowning in Data: The Challenge of Meaning
• Text Mining: Creating Meaning from Large Collections
• Text Mining Markets
What can we do with Text Mining in the Real World? Some case studies
• Document classification: eBay, antiPhish
• Retrieval and Relation Extraction: NZZ, THESEUS CONTENTUS, Fraunhofer Web
• Structuring and Monitoring: EmotionRadar
Conclusion
4
Internet Trends
Ubiquitous intelligent systems
Convergence
Users as producers
5
Users as producers
Web 2.0, Social Web, Crowdsourcing
Exploding growth of content
Media providers transform from content to confidence providers, competing
with social communities
Users expect full interactivity and control
Quality control, confidence, choice and searching are becoming central
8
Drowning in Data ….
Megabytes
Gigabytes
Terabytes
Petabytes
nniviveerrssee::
u
l
a
it
u
ig
l
d
a
SSizizeeooff digit abyte
Ex yte
22000077::116611 Exabyte
Exab yte
22001100::999988 Exab
[[ID
IDCC]]
Exabytes
9
The data iceberg
Database tables
20%
Excel spreadsheets
Other data with fixed structure
Email, Notes
80%
Word documents
PDF. Power Point
Other text
Images
Video, audio
10
Drowning in Unstructured Data ….
Megabytes
Gigabytes
!
g
!
n
g
i
n
n
i
a
n
e
a
e
m
d
m
e
e
d
e
n
e
d
n
annd
Terabytes
…
…a
Petabytes
Exabytes
11
Semantics: The need for meaning
Knowledge will be the driving force
of business excellence
Quality of services increasingly
distinguished by amount of
knowledge they can use
Enormous savings if unstructured
existing documents could be used
Without needing to structure them
first
cf. failures of knowledge
management!
12
The challenge of semantics
intelligent data and text
mining technologies
Very large set
of (electronic)
documents
Manual Structuring
Intelligent Service
13
Outline
Conclusion
14
Text Mining is cool, since … the entire world works for us!
215,675,903 websites (Netcraft, March 2009)
19 200 000 000 webpages (Yahoo, Aug 2005)
29 700 000 000 webpages (boutell.com, Jan 2007)
Google index 26 Million (1998), 1 billion (2000), 1 Trillion 1,000,000,000,000)
unique URLs (25. 7. 2008)
=> perhaps quadrillions of words (images, videos) …
And most of them put together meaningfully (somewhat)!
=> smart algorithms can build on that.
15
The basic idea
If two words occur frequently in the same context
- page, paragraph, sentence, part-of-speech
Then there must be some semantic relation between them
Add in a lot of statistics, algorithms, intelligence…
raw
raw material
material (web,
(web, documents,
documents, …)
…)
++ correlations
and
correlations
and
statistics
AND YOU CAN
DOstatistics
A LOT!
++ intelligent
intelligent data
data mining
mining algorithms
algorithms
You
You can
can create
create (a
(a bit
bit of)
of) semantics!
semantics!
16
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…
…
17
Unsupervised hierarchical term Clustering: dpa data
Team sport
Spiel Bundesliga Team Trainer
Sieg Mannschaft Niederlage
Samstag Platz Saison Erfolg
Punkte Pokal Nationalspieler …
Football
Not football
Finale Frankfurt deutsche
Meister Hamburg Zuschauer
Zuschauern Männer Halle WM
Titelverteidiger Final EM …
Basketball Berlin Weltmeister
Bonn Kampf K Hagen Trier
Würzburg LBA Playoff Runde
Box Berliner Klitschko Titel…
Basketball + Boxing
Kiel Handball Magdeburg
Flensburg HSG VfL TV THW
Tore Bad Wuppertal Lemgo
Bundesliga Handewitt …
Handball
FC Trainer Fußball München
Spieler Bayern Mannschaft
Saison Hertha Stürmer Stadion
Spiel SV Dortmund Coach …
Minute Tor VfL Schiedsrichter
Bayern League Champions
Zuschauer Minuten Führung Fußball UEFA United Hinspiel
Tore Hansa Eintracht Schalke Cup Manager Vertrag Leeds
Bundesliga Karte Wolfsburg … Club Fans Real Hitzfeld …
German League
European League
18
Text Mining Market Size
„The text mining market has roughly $50-100 million annual product
revenue, and is growing at roughly 40-60% annually.“ [Monash 06, texttechnologies.com]
Sounds small …
But then …
• Several research sites devoted to the technology
…
So the real market must be somewhere else …
19
The Text Mining Market … is called “Text Analytics”
Primary areas:
Web search, site search
knowledge management, enterprise portals
Information collection, extraction, harvesting
Email handling, security, spam and phishing filtering
Market research
Online advertising
Specialized markets
• litigation, juridical
• Patent search
[cf. Monash/2008]
20
Application Field Market Research: Germany 1.6 billion, growing
Both ad-hoc studies and panels can benefit from text mining
http://www.adm-ev.de/zahlen.html
21
Enterprise Search as a text mining market
More than 1.2 billion $ in 2010
Year
2006
2007
2008
2009
2010
Software
revenue
Million $
717
860
989
1108
1219
[Gartner 2008]
22
Companies
23
Outline
• Retrieval and Relation Extraction: NZZ, THESEUS CONTENTUS, Wikinger, Fraunhofer
Web
• Structuring and Monitoring: Semantic Map, EmotionRadar
Conclusion
24
Text Mining Tasks
Document classification, scoring and/or ranking, isolated retrieval
• Assign a class, score or rank to an entire document
In-collection, linked retrieval and organization
• Find documents in a collection
• Link results to other results
Information and relation extraction
• Extract pieces of information, fill particular relations
Overview and monitoring of collections
• Give summary impression of information in a collection or source
25
Outline
Conclusion
26
Motivation
Spotting Faked Offers at Internet Auctions
Techniques to sell fakes
Put faked products on an internet
auction platform, e.g. ebay
Describe product as forged,
falsified, e.g. “very similar to XXX”
Aspects
Infringement of registered trade
marks
Violation of patents
Enormous sales volume
27
Counter Measures
Motivation
Use trainable classifiers
Compile training set of genuine and
faked internet auction offers
Train classifiers to detect these
classes
use text, format information, etc. as
features
Fakes
x2
Hy
pe
Use different classifier for different
brands / products
Apply to new internet auction
offers
Ban faked offers from auction
rp
lan
Originals
x1
Update classifiers to new techniques
e
28
Results
A classifier was developed and
tested
Similar techniqes as for spam
detection
The Germal Federal Court of Justice:
Internet Auction providers have to
filter the auctions using approriate
methods to detect faked offers.
Good results: F-value >> 90%
29
Motivation
Phishing
E-mail fraud
Send officially looking
email
Include web link or form
Ask for confidential
information
e.g. password, account
details
Attacker uses information
to withdraw money, enter
computer system, etc.
30
Motivation
AntiPhish
Project AntiPhish
Consortium
Develop content-based phishing filters Fraunhofer IAIS (DE)
Include other clues like whitelists
Symantec (GB, IRL)
Trainable and adaptive filters
Î adapt to new phishing attacks
Î anticipate attacks
Tiscali (IT)
Nortel (FR)
K.U. Leuven (BE)
32
Motivation
Phishing: Defense Techniques
Workflow
Obtain training data from email
stream
Integrate new filters into email
filtering framework
Extract features
Deploy at internet service provider
Estimate and update classifiers and
filters
Deploy at central wireless packet
switch
33
Approach: Multiple feature sets
37
Basic Features
38
Dynamic Markov Chains
39
DMC Details
40
Latent Topic Models
41
Class-Specific Topic Models
42
Feature Processing and Selection
43
Test Corpora
46
Overall Result
47
Outline
Conclusion
52
The THESEUS research program in Germany [theseus-program.com]
Deutsche
Nationalbibliot
hek
Deutsche Thomson
OHG (DTO)
semantic
Deutsches
Forschungszentr
um für
Künstliche
Intelligenz
(DFKI GmbH)
empolis GmbH
Festo AG
FraunhoferGesellschaft (7
Institutes)
Friedrich-AlexanderUniversität
Erlangen
FZI Forschungszentrum
Informatik
Institut für
Rundfunktechni
k GmbH (IRT)
intelligent views gmbh
Ludwig-MaximiliansUniversität
(LMU)
moresophy GmbH
syntactic
LYCOS Europe
mufin GmbH
ontoprise GmbH
SAP AG
Siemens AG
Wess/07
Technische Universität
Darmstadt
Dresden
Single author
Multiple authors
München
Universität Karlsruhe
(TH)
Verband Deutscher
Maschinen- und
Anlagebau e.V.
(VDMA)
53
The THESEUS use cases
ALEXANDRIA
ALEXANDRIA
CONTENTUS
CONTENTUS
MEDICO
MEDICO
The
TheInternet
InternetKnowledge
KnowledgePlatform
Platform
Next
NextGeneration
GenerationDigital
DigitalLibraries
Libraries
for
saving
our
cultural
for saving our culturalheritage
heritage
Semantic
Semanticimage
imageSearch
Search
ininMedicine
Medicine
ORDO
ORDO
PROCESSUS
PROCESSUS
TEXO
TEXO
Personal
PersonalOrdered
OrderedKnowledge
Knowledge
Management
Management
Semantic
SemanticBusiness
BusinessProcesses
Processes
Business
BusinessWebs
Websininthe
theInternet
Internet
Of
OfThings
Things
54
CONTENTUS - Next Generation Digital Libraries
for saving our cultural heritage
Publishers, Libraries, broadcasters, etc. are interested in using,
distributing and saling their archive content
In analog form archives are threatened by deterioration, are not
linked, difficult to use, and huge.
Goals:
Digitalization, optimization of quality, availability
Indexing, semantic and social linking and intelligent search,
communities
Rescue of cultural heritage, preventing losses from deterioration
Laufzeit bis
2012
55
Showcases Semantic Digital Libraries
225 years Neue Zürcher Zeitung NZZ
GDR music archive German National Library
56
CONTENTUS Workflow
Workflow
Data generation: registered
users / communities,
Data generation:
registered
algorithms
with acceptable
users / communities,
quality
algorithms
with
Quality
control:
selfacceptable
control
(see quality
Wikipedia)
Quality control: self control
(see Wikipedia)
Shell
Mantle
Core
1
2
Digitization
4
3
Automated
generation of
metadata
Automated
optimization of
quality
5
Controlled quality
Data generation: automatically
Controlled
quality
generated
through
high-quality
Data generation: automatically
algorithms
generated
through
high-quality
Quality
control:
training
and
algorithms
improvement
of algorithms
Quality control: training and
improvement of algorithms
Guaranteed quality
Data generation and
Guaranteed
quality
correction:
Libraries,
museums,
Data generation
and
universities,
experts, etc.
correction:
museums,
Quality
control:Libraries,
Schooling,
experts, etc.
rules,universities,
advisory boards
Quality
control:
Schooling,
Highest
stability,
highest
rules,
advisory
boards
persistence
Highest stability, highest
persistence
6
Open
knowledge
networks – user
augmentation
Semantic
linking of
metadata
Semantic
access to
knowledge
and content
57
Digitalisierung
1
2
Digitization
Automatic
Optimization
of quality
3
Automated
Generation of
metadata
4
Semantic
Linking of
metadata
5
Open
knowledge
networks –
user
augmentation
6
Semantic
access to
knowledge
and content
High-Throughput
Methods
Modern book
scanners:
Thousands of pages
per day
Almost fully
automatic
Data volumes: 70TB
(NZZ), Peta-Exabytes
(DNB)
58
Digitalisierung
1
Digitalisierung
Digitization
High-Throughput
Methods
Modern book
scanners:
Thousands of pages
per day
Almost fully
automatic
Data volumes: 70TB
(NZZ), Peta-Exabytes
(DNB)
59
Qualitätsoptimierung
1
Digitalisierung
Digitalization
2
Automated
Optimization
of quality
Development of
intelligent
algorithms for
optimizing print,
images, sound &
movies
Automated
generation of
presentation formats
Margin removal
Sharpening,
Straightening
Denoising, declicking
Scratch removal
60
Metadatengenerierung
1
2
Digitalisierung
Digitalization
Automated
Optimization
of quality
3
Automated
Generation of
metadata
Structural and
contentual metadata
OCR, speech, music,
video recognition
Structure analysis and
type recognition
Linking with current
norms & standards
61
Semantische Vernetzung
1
Digitalisierung
Digitalization
2
Automated
Optimization
of quality
3
Automated
Generation of
metadata
4
Semantic
linking of
contents
Link-up with related
media
Incorporation of external
knowledge sources
(metadata systems,
Wikipedia, …)
Disambiguation,
classification, relation
extraction
62
Determining meaning
The words of natural
language are often
ambiguous
Über Kohl höhnte Strauß: „Er wird nie Kanzler
werden. Die Zeit, 18.7.08
» For each word / term, find a meaning
» Subproblem:
» Part of speech recognition: Nouns, Verb, Adjective, …
» Named entity recognition: People, Places, Organizations, …
» Assignment of concepts: Plant, Bird, Politician, …
63
Named entity recognition
» Analyze
Surroundings of
Words
Über Kohl höhnte Strauß: „Er wird nie Kanzler
werden.
» “Kohl” in a sentence with “Kanzler” Î probably “person”
» “Kohl” in a sentence with “kochen” Î probably “vegetable”
» Statistical model for person names
» Word + Surroundings -> word is a person
» Training using annotated sentences.
» Automatic Recognition of
words / phrases that represent people
64
Conditional Random Field Model
» Observed words X1,…,Xn
» Category of words Y1,…,Yn
⎛ N ⎡ N2
⎤⎞
1
p (Y1,K, Yn | X ) =
exp⎜ ∑ ⎢ ∑ λk f k ,C (Yt −1, Yt , X )⎥ ⎟
⎜
⎟
Z (X, λ , μ )
⎦⎠
⎝ t =1⎣k =1
» Properties f may depend on two subsequent states and on all observed words
Example
» Property f10293 has value 1,
- if Yt-1=“PER" and Yt=“PER” and
- Xt has value “Müller”.
Otherwise its value is 0.
[Lafferty, McCallum, Pereira 01]
66
Modeling of names: features for a CRF model
» Title FirstName Connective LastName
» Properties. Recorded for the words xt-2,xt-1,xt,xt+1,xt+2
» Words, stem, part of speech
» Prefix, Suffix (3 letters)
» Shape properties
Capital characters at the beginning, only numbers, contains numbers, mix capital /no
capital, contains hyphens
» LDA topic model class
» Contained in list of first names, contained in list of last names
it
rbe
A
In
67
Identity of names
» There are several people named “Helmut Kohl”
» Helmut Kohl, born 1930, Chancellor
» Helmut Kohl, born 1943, Referee
» Helmut Kohl, textile merchand
» … 99 further hits in the telephone book
» Identification in Wikipedia
» Compare words of Wikipedia-article
with the text in which
“Helmut Kohl” was found
» Similar words -> similar person
» Automated assignment:
Person name -> Wikipedia article
68
Assignment of similarity from the environment
Simple algorithm for assigning people to Wikipedia article
» Occurence in text: Helmut Kohl
» Description using characteristic terms -> x
» Wikipedia article on Kohl
» Description by characteristic terms -> w
» Comparison using a distance metric: for example Cosine distance d(w,u)
» Implemented in a prototype
» Further approach: Assignment as a classification task
f(w,u) = 0 or 1
Master Thesis
70
Semantische Interpretation
Semantic Interpretation
Currently assign semantic categories in the Contentus Prototype
» Names: People, Organizations, points in time, places, …
» Assignment to Wikipedia articles
Under development:
» Hypernyms in ontology (GermaNet): Nouns, Verbs Î Supersenses
» Cluster of words with similar meaning: Topics
» Relations between names / concepts
“Berthold Brecht” studied in “München”
» Classes of documents: Politics, Economy, …
72
Knowledge store
» Further information for entities that
were found in the text
» Dates, publications
» Number of inhabitants, topological
relationships
Helmut Kohl
Geburtsdatum
30.4.1930
Geburtsort
Ludwigshafen
Ehegatte
Hannelore K.
Ausbildung
Historiker
Religion
katholisch
Partei
CDU
Berlin
Fläche
» Social networks
» Who knows whom?
» Who was at the same place at the same time?
» Who influenced whom?
Einwohner
BIP
Höhe
891 km2
3.420.786
83,6 Mrd. €
34–115 m
Geo. Breite
52° 31′ N
Geo. Länge
13° 25′ O
87
Knowledge store: Format
» Factual knowledge as logical expressions:
» <subject> <predicate> <object>
» Semantic-Web-Standards
» RDF
» RDFS
» OWL
» Technical Basis
» Database MySQL
» Triple-Store Jena + Joseki
» Query language
» SPARQL
88
Buchmesse Frankfurt 10.Oktober 2008 | 88
Wissensvernetzung
Linking of knowledge
» Semantic Integration of data and information from different sources
» DBPedia: an interpreted form of Wikipedia
» Geonames Ontology: all the places in the world
» Catalogue of the German national library:
Books and publications
Î Triplestore
» Based on open standards
» W3C Semantic Web Stack
» RDF, RDFS, OWL, SPARQL
89
Knowledge sources
» DBPedia (www.dbpedia.org)
» GeoNames Ontology
» Already in RDF/OWL-Format
» Person reference database PND
» Topic reference database SWD
» Online catalogue OPAC
» Partial export to RDF
» Found entities in the text
» Identification using Wikipedia
» Linking with DBPedia-Daten per Link
90
Buchmesse Frankfurt 10.Oktober 2008 | 90
Offene Wissensnetzwerke
2
1
Digitalisierung
Digitalization
Automated
Optimization
of quality
3
Automated
Generation of
metadata
4
Semantic
linking of
metadata
5
Open
knowledge
networks –
user
augmentation
Further annotations from experts and users
• Completions, corrections
• Cooperation with the ALEXANDRIA project in Theseus
• Suitable measures to assure high quality of data
91
Offene Wissensnetzwerke
The Multiple Shell Model
Cf. Wikinger [Bröcker et.al. 08]!
Open
Openknowledge
knowledgenetwork
network
Data
generation:
Registered
Data generation: Registeredusers
users/ /Communities,
Communities,
Algorithms
Algorithms
Quality
Qualitycontrol:
control:Self
Selfcontrol
control(cf.
(cf.Wikipedia)
Wikipedia)
Outer
Mantel
Controlled
ControlledQuality
Quality
Data
generation:
Data generation:Algorithms
Algorithmsof
ofhigh
highquality
quality
Quality
control:
Training
and
improvement
Quality control: Training and improvementof
of
algorithms
algorithms
Core
Assured
Assuredquality
quality
Data
generation
Data generationand
andcorrection:
correction:Libraries,
Libraries,
Universities,
Museums,
groups
of
experts,
Universities, Museums, groups of experts,etc.
etc.
Quality
control:
Fixed
rules,
committes,
Quality control: Fixed rules, committes,
92
maximal
maximalStability
Stabilityand
andPersistence
Persistence
Semantische Suche
2
1
Digitalisierung
Digitalization
Automated
Optimization
of quality
3
Automated
4
Generation of
metadata
Semantic
linking of
metadata
5
Open
knowledge
networks –
user
augmentation
6
Semantic
access to
knowledge
and content
The knowledge network
• Digital, multimedia data
• Content is semantically linked
• Is enriched from external sources and
user groups
Access
• Structure by Ontology
• Content relationships become clear
• “Knowledge exploration” is possible
93
The Contentus Demonstrator
95
Outline
Conclusion
102
Example of a classified webpage
Übereinstimmung zu dem Dokumentmodell = 80%
Klassifikation als = Projekte
107
Workflow for semantic processing of documents
!!
9
9
0
22000
PreCategoriza
Entity
r
r
e
Extracted
e
processing
tion
Recognitio
m
m
m
Search index
Metadata
n
m
u
s
u
s
iinn
h
c
h
nnc
u
Crawl
u
a
l
eela
Documents
r
r
e
tthhe
r
r
o
ffo Using the document model
t
t
u
oou
h
c
h
aattc
W
W
Extracted
Knowledge
Store
Search
regions
Using the structure
model
111
Outline
Conclusion
112
Emotion Radar
Which issues are important to
people, where are the
emotional discussions in
blogs and discussion
forums?
Goal: market research, …
113
Emotion Radar example: Looking at two large automobil companies A and
B*

Selection and Crawling of discussion forums
•
Criteria: Search engine ranking, size, activity
•
Period used: January 2008 to January 2009
•
Storage: 2 GB of data crawl during a period of 7 days
Structure analysis of the discussion forums:
•
Manufacturer A:
–
–
–
–
•
Number of postings: 188.487
Monthly number of new postings: ca. 1.500
Number of threads: 21.613
Number of authors: 15.445
Manufacturer B:
–
–
–
–
Number of postings: 406.814
Monthly number of new postings: ca. 2.700
Number of threads: 38.758
Number of authors: 21.919
* anonymisiert
114
Case study: Internet postings related to the introduction of a new car
model in Germany 2008
Cars are delivered
Manufacturer publishes
further product
features/start of sales
Manufacturer
publishes first
pictures
Dr. Stefan Wrobel
*Prof.
anonymisiert
115
Partially automated emotion analysis shows a mood swing from positive to
negative („in love-> angry“)
Cars are delivered
„angry“
„angry“
− −
„proud“
„proud“
− −
„angry“
„angry“
− −
Manufacturer publishes
further product
features/start of sales
Manufacturer
publishes first
pictures
„surprised“
„surprised“
− −
„turned
off“
„turned
off“
− −
„in
love“
„in
love“
„hoping“
„hoping“
− −
„interested“
„interested“
− −
116
Dr. Stefan Wrobel
*Prof.
anonymisiert
Topic recognition shows a change of product features that are discussed from
design to gasoline consumption
Auslieferungen
Probefahrten
Probefahrten
− −
Verbrauch
Verbrauch
− −
„verärgert“
„verärgert“
− −
Preise
CarPreise
CarKonfigurator
Konfigurator
und
Liste
und Liste
− −
Chromleisten,
Chromleisten,
Wertanmutung
Wertanmutung
− −
Erste Fotos
„Riesen„Riesenfischmaul“
fischmaul“
− −
Schaltung,
Schaltung,
Effizienz,
Effizienz,
Technologie
Technologie
− −
FahrzeugFahrzeuglänge,
Design
länge,
Design
− −
„verliebt“
„verliebt“
− −
− −
− −
Dr. Stefan Wrobel
*Prof.
anonymisiert
Verbrauch
Verbrauch
Schiebedach,
Schiebedach,
Lenkrad,
Lenkrad,
Bordcomputer,
Bordcomputer,
Audiosystem
Audiosystem
− −
„abgestoßen“
„abgestoßen“
− −
Verbrauch,
Verbrauch,
Klappschlüssel,
Klappschlüssel,
Audio
Audio
− −
„verärgert“
„verärgert“
− −
Nachbarn
Nachbarn
− −
„stolz“
„stolz“
− −
„überrascht“
„überrascht“
− −
„...kaum zu glauben
„...kaum zu glauben
was dieses kleine
was dieses kleine
Auto an Benzin
Auto an Benzin
verbraucht!“
verbraucht!“
(Kalle83)
(Kalle83)
„zugeneigt“
„zugeneigt“
− −
„hoffend“
„hoffend“
117
How can manufacturers use these text mining results?
Auslieferungen
e.g.
Z.B.
short
durch
term
eine
recognition
frühzeitigeof relevant
Probefahrten
Probefahrten
− −
topics
Erkennung
(consumption)
relevanter
andThemen
preparation
Verbrauch
Verbrauch
− −
of(Verbrauch)
appropriateund
resonse
Ableiten
(gasvon
saver
„verärgert“
„verärgert“
− −
trainings,
Maßnahmen
fuel efficient
(Spritspartrainings,
tires, proactive
communikation)
Leichtlaufreifen, Kommunikation)
„verärgert“
„verärgert“
− −
Preise
CarPreise
CarKonfigurator
Konfigurator
und
Liste
und
Liste
− −
Chromleisten,
Chromleisten,
Wertanmutung
Wertanmutung
− −
Verbrauch,
Verbrauch,
Klappschlüssel,
Klappschlüssel,
Audio
Audio
− −
Nachbarn
Nachbarn
− −
„stolz“
„stolz“
− −
„überrascht“
„überrascht“
− −
„Riesen„Riesenfischmaul“
fischmaul“
− −
Schaltung,
Schaltung,
Effizienz,
Effizienz,
Technologie
Technologie
− −
FahrzeugFahrzeuglänge,
Design
länge,
Design
− −
„verliebt“
„verliebt“
− −
− −
− −
Dr. Stefan Wrobel
*Prof.
anonymisiert
Verbrauch
Verbrauch
„hoffend“
„hoffend“
Schiebedach,
Schiebedach,
Lenkrad,
Lenkrad,
Bordcomputer,
Bordcomputer,
Audiosystem
Audiosystem
− −
„zugeneigt“
„zugeneigt“
− −
„abgestoßen“
„abgestoßen“
− −
... Long term continuous
monitoring of emotional topics
118
119
Summary
Text Mining is cool!
We can do a lot with Text Mining in the Real World!
• Retrieval and Relation Extraction: NZZ, THESEUS CONTENTUS, Fraunhofer
Web
120
The fine print: Papers and further reading

Horváth, Tamás; Ramon, Jan:Efficient frequent connected subgraph mining in
graphs of bounded treewidth: Machine learning and knowledge discovery in
database: ECML PKDD 2008. Berlin [u.a.]: Springer, 2008. (Machine learning and
knowledge discovery in databases 1), S. 520-535

Kolb, Inke; Deutschland / Bundesbeauftrager für Kultur und Medien; FraunhoferInstitut IAIS: Auf dem Weg zur Deutschen Digitalen Bibliothek (DDB): erstellt im
Auftrag des Beauftragten der Bundesregierung für Kultur und Medien. 2008

Köhler, Joachim; Larson, Martha; Jong, Franciska de Jong; Kraaij, Wessel;
Ordelman, Roeland; Association for Computing Machinery / Special Interest Group
on Information Retrieval: Proceedings of the ACM SIGIR Workshop "Searching
Spontaneous Conversational Speech": held in conjunction with the 31th Annual
International ACM SIGIR Conference 24 July 2008, Singapore, 2008

Krausz, Barbara; Herpers, Rainer: Event detection for video surveillance using an
expert system In: Association for Computing Machinery / Special Interest Group on
Multimedia: 1st ACM International Workshop in Analysis and Retrieval of
Events/Actions and Workflows in Video Streams (AREA 2008): October 31, 2008,
Vancouver, Canada ; in conjunction with ACM Multimedia 2008. New York, NY:
ACM, 2008, S. 49-55

Lioma, Christina; Moens, Marie-Francine; Gomez, Juna-Carlos; De Beer, Jan;
Bergholz, Andre; Paass, Gerhard; Horkan, Patrick: Anticipating Hidden Text Salting
in Emails: extended abstract. In: Lippmann, Richard (Ed.) et al.: Recent advances in
intrusion detection: 11th international symposium, RAID 2008, Cambridge, MA,
USA, September 15-17, 2008 ; proceedings. Berlin [u.a.]: Springer, 2008.

Anja Pilz, Lukas Molzberger, and Gerhard Paa. Entity resolution by kernel methods.
In Proc. Sabre TMS, 2009.

Andre Bergholz, Jan De Beer, Sebastian Glahn, Marie-Francine Moens, Gerhard
Paass, Siehyun Strobel. New Filtering Approaches for Phishing Email. Accepted for
publication for Journal of Computer Security (JCS)

Gerhard Paass and Frank Reichartz (2009): Exploiting Semantic Constraints for
Estimating Supersenses with CRFs. Proc. SDM 2009 (accepted for publication)

Paaß, Gerhard; Reinhardt, Wolf; Rüping, Stefan; Wrobel, Stefan: Data
mining for security and crime detection In: Gal, Cecilia S. (Ed.) et al.:
Security informatics and terrorism: social and technical problems of
detecting and controlling terrorists' use of the World Wide Web ;
proceedings of the NATO Advanced Research Workshop on Security
Informatics and Terrorism - Patrolling the Web, Beer-Sheva, Israel, 4-5
June 2007. Amsterdam [u.a.]: IOS Press, 2008. (NATO ASI series : Series
D, Information and Communication Security 15), S. 56-70

Paaß, Gerhard; Kindermann, Jörg: Entity and relation extraction in texts
with semi-supervised extensions In: Gal, Cecilia S. (Ed.) et al.: Security
informatics and terrorism: social and technical problems of detecting
and controlling terrorists' use of the World Wide Web ; proceedings of
the NATO Advanced Research Workshop on Security Informatics and
Terrorism - Patrolling the Web, Beer-Sheva, Israel, 4-5 June 2007.
Amsterdam [u.a.]: IOS Press, 2008. (NATO ASI series : Series D,
Information and Communication Security 15), S. 132-141

Frank Reichartz and Gerhard Paaß. Estimating Supersenses with
Conditional Random Fields. Workshop on High-Level Information
Extraction, ECML/PKDD 2008.

Andre Bergholz, Gerhard Paass, Frank Reichartz, Siehyun Strobel,
Marie-Francine Moens and Brian Witten: Detecting Known and New
Salting Tricks in Unwanted Emails Fifth Conference on Email and AntiSpam, CEAS 2008, Aug 21-22, 2008

Andre Bergholz,Jeong-Ho Chang, Gerhard Paass, Frank Reichartz and
Siehyun Strobel. Improved Phishing Detection using Model-Based
Features. Fifth Conference on Email and Anti-Spam, CEAS 2008, Aug 2122, 2008, Mountain View, Ca.

Stefan Eickeler, Lars Br¨ocker, and Ruth Haener. NZZ: 225 Jahre Old
economy vernetzt - Realisierung des digitalen Archivs der Neuen
Zürcher Zeitung. In GI Jahrestagung, pages 73–77, 2005.
121