Information Extractionand Integration: an Overview
William W. CohenCarnegie Mellon University
April 26, 2004
Example: The Problem
Martin Baker, a person
Genomics job
Employers job posting form
Example: A Solution
Extracting Job Openings from the Web
foodscience.com-Job2
JobTitle: Ice Cream Guru
Employer: foodscience.com
JobCategory: Travel/Hospitality
JobFunction: Food Services
JobLocation: Upper Midwest
Contact Phone: 800-488-2611
DateExtracted: January 8, 2001
Source: www.foodscience.com/jobs_midwest.html
OtherCompanyJobs: foodscience.com-Job1
Job
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Cat
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Fo
od
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.
IE from Research Papers
What is “Information Extraction”
Filling slots in a database from sub-segments of text.As a task:
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers.
"We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“
Richard Stallman, founder of the Free Software Foundation, countered saying…
NAME TITLE ORGANIZATION
What is “Information Extraction”
Filling slots in a database from sub-segments of text.As a task:
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers.
"We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“
Richard Stallman, founder of the Free Software Foundation, countered saying…
NAME TITLE ORGANIZATIONBill Gates CEO MicrosoftBill Veghte VP MicrosoftRichard Stallman founder Free Soft..
IE
What is “Information Extraction”
Information Extraction = segmentation + classification + clustering + association
As a familyof techniques:
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers.
"We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“
Richard Stallman, founder of the Free Software Foundation, countered saying…
Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation
aka “named entity extraction”
What is “Information Extraction”
Information Extraction = segmentation + classification + association + clustering
As a familyof techniques:
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers.
"We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“
Richard Stallman, founder of the Free Software Foundation, countered saying…
Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation
What is “Information Extraction”
Information Extraction = segmentation + classification + association + clustering
As a familyof techniques:
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers.
"We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“
Richard Stallman, founder of the Free Software Foundation, countered saying…
Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation
What is “Information Extraction”
Information Extraction = segmentation + classification + association + clustering
As a familyof techniques:
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers.
"We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“
Richard Stallman, founder of the Free Software Foundation, countered saying…
Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation N
AME
TITLE ORGANIZATION
Bill Gates
CEO
Microsoft
Bill Veghte
VP
Microsoft
Richard Stallman
founder
Free Soft..
*
*
*
*
Tutorial Outline
• IE History• Landscape of problems and solutions• Models for named entity recognition:
– Sliding window– Boundary finding– Finite state machines
• Overview of related problems and solutions– Association, Clustering– Integration with Data Mining
IE HistoryPre-Web• Mostly news articles
– De Jong’s FRUMP [1982]• Hand-built system to fill Schank-style “scripts” from news wire
– Message Understanding Conference (MUC) DARPA [’87-’95], TIPSTER [’92-’96]
• Early work dominated by hand-built models– E.g. SRI’s FASTUS, hand-built FSMs.– But by 1990’s, some machine learning: Lehnert, Cardie, Grishman and
then HMMs: Elkan [Leek ’97], BBN [Bikel et al ’98]Web• AAAI ’94 Spring Symposium on “Software Agents”
– Much discussion of ML applied to Web. Maes, Mitchell, Etzioni.• Tom Mitchell’s WebKB, ‘96
– Build KB’s from the Web.• Wrapper Induction
– Initially hand-build, then ML: [Soderland ’96], [Kushmeric ’97],…– Citeseer; Cora; FlipDog; contEd courses, corpInfo, …
IE History
Biology• Gene/protein entity extraction• Protein/protein fact interaction• Automated curation/integration of databases
– At CMU: SLIF (Murphy et al, subcellular information from images + text in journal articles)
Email• EPCA, PAL, RADAR, CALO: intelligent office
assistant that “understands” some part of email– At CMU: web site update requests, office-space
requests; calendar scheduling requests; social network analysis of email.
www.apple.com/retail
IE is different in different domains!Example: on web there is less grammar, but more formatting & linking
The directory structure, link structure, formatting & layout of the Web is its own new grammar.
Apple to Open Its First Retail Storein New York City
MACWORLD EXPO, NEW YORK--July 17, 2002--Apple's first retail store in New York City will open in Manhattan's SoHo district on Thursday, July 18 at 8:00 a.m. EDT. The SoHo store will be Apple's largest retail store to date and is a stunning example of Apple's commitment to offering customers the world's best computer shopping experience.
"Fourteen months after opening our first retail store, our 31 stores are attracting over 100,000 visitors each week," said Steve Jobs, Apple's CEO. "We hope our SoHo store will surprise and delight both Mac and PC users who want to see everything the Mac can do to enhance their digital lifestyles."
www.apple.com/retail/soho
www.apple.com/retail/soho/theatre.html
Newswire Web
Landscape of IE Tasks (1/4):Degree of Formatting
Text paragraphswithout formatting
Grammatical sentencesand some formatting & links
Non-grammatical snippets,rich formatting & links Tables
Astro Teller is the CEO and co-founder of BodyMedia. Astro holds a Ph.D. in Artificial Intelligence from Carnegie Mellon University, where he was inducted as a national Hertz fellow. His M.S. in symbolic and heuristic computation and B.S. in computer science are from Stanford University. His work in science, literature and business has appeared in international media from the New York Times to CNN to NPR.
Landscape of IE Tasks (2/4):Intended Breadth of Coverage
Web site specific Genre specific Wide, non-specific
Amazon.com Book Pages Resumes University Names
Formatting Layout Language
Landscape of IE Tasks (3/4):Complexity
Closed set
He was born in Alabama…
Regular set
Phone: (413) 545-1323
Complex pattern
University of ArkansasP.O. Box 140Hope, AR 71802
…was among the six houses sold by Hope Feldman that year.
Ambiguous patterns,needing context andmany sources of evidence
The CALD main office can be reached at 412-268-1299
The big Wyoming sky…
U.S. states U.S. phone numbers
U.S. postal addresses
Person names
Headquarters:1128 Main Street, 4th FloorCincinnati, Ohio 45210
Pawel Opalinski, SoftwareEngineer at WhizBang Labs.
E.g. word patterns:
Landscape of IE Tasks (4/4):Single Field/Record
Single entity
Person: Jack Welch
Binary relationship
Relation: Person-TitlePerson: Jack WelchTitle: CEO
N-ary record
“Named entity” extraction
Jack Welch will retire as CEO of General Electric tomorrow. The top role at the Connecticut company will be filled by Jeffrey Immelt.
Relation: Company-LocationCompany: General ElectricLocation: Connecticut
Relation: SuccessionCompany: General ElectricTitle: CEOOut: Jack WelshIn: Jeffrey Immelt
Person: Jeffrey Immelt
Location: Connecticut
Landscape of IE Techniques (1/1):Models
Any of these models can be used to capture words, formatting or both.
Lexicons
AlabamaAlaska…WisconsinWyoming
Abraham Lincoln was born in Kentucky.
member?
Classify Pre-segmentedCandidates
Abraham Lincoln was born in Kentucky.
Classifier
which class?
Sliding Window
Abraham Lincoln was born in Kentucky.
Classifier
which class?
Try alternatewindow sizes:
Boundary Models
Abraham Lincoln was born in Kentucky.
Classifier
which class?
BEGIN END BEGIN END
BEGIN
Context Free Grammars
Abraham Lincoln was born in Kentucky.
NNP V P NPVNNP
NP
PP
VP
VP
S
Mos
t lik
ely
pars
e?
Finite State Machines
Abraham Lincoln was born in Kentucky.
Most likely state sequence?
Sliding Windows
Extraction by Sliding Window
GRAND CHALLENGES FOR MACHINE LEARNING
Jaime Carbonell School of Computer Science Carnegie Mellon University
3:30 pm 7500 Wean Hall
Machine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.
CMU UseNet Seminar Announcement
E.g.Looking forseminarlocation
Extraction by Sliding Window
GRAND CHALLENGES FOR MACHINE LEARNING
Jaime Carbonell School of Computer Science Carnegie Mellon University
3:30 pm 7500 Wean Hall
Machine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.
CMU UseNet Seminar Announcement
E.g.Looking forseminarlocation
Extraction by Sliding Window
GRAND CHALLENGES FOR MACHINE LEARNING
Jaime Carbonell School of Computer Science Carnegie Mellon University
3:30 pm 7500 Wean Hall
Machine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.
CMU UseNet Seminar Announcement
E.g.Looking forseminarlocation
Extraction by Sliding Window
GRAND CHALLENGES FOR MACHINE LEARNING
Jaime Carbonell School of Computer Science Carnegie Mellon University
3:30 pm 7500 Wean Hall
Machine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.
CMU UseNet Seminar Announcement
E.g.Looking forseminarlocation
A “Naïve Bayes” Sliding Window Model[Freitag 1997]
00 : pm Place : Wean Hall Rm 5409 Speaker : Sebastian Thrunw t-m w t-1 w t w t+n w t+n+1 w t+n+m
prefix contents suffix
If P(“Wean Hall Rm 5409” = LOCATION) is above some threshold, extract it.
… …
Estimate Pr(LOCATION|window) using Bayes rule
Try all “reasonable” windows (vary length, position)
Assume independence for length, prefix words, suffix words, content words
Estimate from data quantities like: Pr(“Place” in prefix|LOCATION)
“Naïve Bayes” Sliding Window Results
GRAND CHALLENGES FOR MACHINE LEARNING
Jaime Carbonell School of Computer Science Carnegie Mellon University
3:30 pm 7500 Wean Hall
Machine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.
Domain: CMU UseNet Seminar Announcements
Field F1 Person Name: 30%Location: 61%Start Time: 98%
SRV: a realistic sliding-window-classifier IE system
• What windows to consider?– all windows containing as many tokens as the shortest
example, but no more tokens than the longest example
• How to represent a classifier? It might:– Restrict the length of window;– Restrict the vocabulary or formatting used
before/after/inside window;– Restrict the relative order of tokens;– Use inductive logic programming techniques to express all
these…<title>Course Information for CS213</title>
<h1>CS 213 C++ Programming</h1>
[Frietag AAAI ‘98]
SRV: a rule-learner for sliding-window classification
• Primitive predicates used by SRV:– token(X,W), allLowerCase(W), numerical(W), …– nextToken(W,U), previousToken(W,V)
• HTML-specific predicates:– inTitleTag(W), inH1Tag(W), inEmTag(W),…– emphasized(W) = “inEmTag(W) or inBTag(W) or …”– tableNextCol(W,U) = “U is some token in the column
after the column W is in”– tablePreviousCol(W,V), tableRowHeader(W,T),…
SRV: a rule-learner for sliding-window classification
• Non-primitive “conditions” used by SRV:– every(+X, f, c) = for all W in X : f(W)=c
– some(+X, W, <f1,…,fk>, g, c)= exists W: g(fk(…(f1(W)…))=c
– tokenLength(+X, relop, c):– position(+W,direction,relop, c):
• e.g., tokenLength(X,>,4), position(W,fromEnd,<,2)
courseNumber(X) :- tokenLength(X,=,2), every(X, inTitle, false), some(X, A, <previousToken>, inTitle, true),some(X, B, <>. tripleton, true)
Non-primitive conditions make greedy search easier
Rapier – results vs. SRV
Rule-learning approaches to sliding-window classification: Summary
• SRV, Rapier, and WHISK [Soderland KDD ‘97]
– Representations for classifiers allow restriction of the relationships between tokens, etc
– Representations are carefully chosen subsets of even more powerful representations based on logic programming (ILP and Prolog)
– Use of these “heavyweight” representations is complicated, but seems to pay off in results
• Some questions to consider:– Can simpler, propositional representations for classifiers
work (see Roth and Yih)– What learning methods to consider (NB, ILP, boosting,
semi-supervised – see Collins & Singer)– When do we want to use this method vs fancier ones?
BWI: Learning to detect boundaries
• Another formulation: learn three probabilistic classifiers:– START(i) = Prob( position i starts a field)– END(j) = Prob( position j ends a field)– LEN(k) = Prob( an extracted field has length k)
• Then score a possible extraction (i,j) bySTART(i) * END(j) * LEN(j-i)
• LEN(k) is estimated from a histogram
[Freitag & Kushmerick, AAAI 2000]
BWI: Learning to detect boundaries
Field F1 Person Name: 30%Location: 61%Start Time: 98%
Problems with Sliding Windows and Boundary Finders
• Decisions in neighboring parts of the input are made independently from each other.
– Expensive for long entity names– Sliding Window may predict a “seminar end time” before the
“seminar start time”.– It is possible for two overlapping windows to both be above
threshold.
– In a Boundary-Finding system, left boundaries are laid down independently from right boundaries, and their pairing happens as a separate step.
Finite State Machines
IE with Hidden Markov Models
Yesterday Pedro Domingos spoke this example sentence.
Yesterday Pedro Domingos spoke this example sentence.
Person name: Pedro Domingos
Given a sequence of observations:
and a trained HMM:
Find the most likely state sequence: (Viterbi)
Any words said to be generated by the designated “person name”state extract as a person name:
),(maxarg osPs
person name
location name
background
HMM for Segmentation
• Simplest Model: One state per entity type
What is a “symbol” ???
Cohen => “Cohen”, “cohen”, “Xxxxx”, “Xx”, … ?
4601 => “4601”, “9999”, “9+”, “number”, … ?
000.. . . .999
3 -d ig i ts
00000 .. . .99999
5 -d ig i ts
0 ..99 0000 ..9999 000000 ..
O th e rs
N u m b e rs
A .. ..z
C h a rs
a a ..
M u lt i -le tte r
W o rds
. , / - + ? #
D e lim ite rs
A ll
Datamold: choose best abstraction level using holdout set
HMM Example: “Nymble”
Other examples of shrinkage for HMMs in IE: [Freitag and McCallum ‘99]
Task: Named Entity Extraction
Train on ~500k words of news wire text.
Case Language F1 .Mixed English 93%Upper English 91%Mixed Spanish 90%
[Bikel, et al 1998], [BBN “IdentiFinder”]
Person
Org
Other
(Five other name classes)
start-of-sentence
end-of-sentence
Transitionprobabilities
Observationprobabilities
P(st | st-1, ot-1 ) P(ot | st , st-1 )
Back-off to: Back-off to:
P(st | st-1 )
P(st )
P(ot | st , ot-1 )
P(ot | st )
P(ot )
or
Results:
What is a symbol?
Bikel et al mix symbols from two abstraction levels
What is a symbol?
Ideally we would like to use many, arbitrary, overlapping features of words.
St -1
St
Ot
St+1
Ot +1
Ot -1
identity of wordends in “-ski”is capitalizedis part of a noun phraseis in a list of city namesis under node X in WordNetis in bold fontis indentedis in hyperlink anchor…
…
…part of
noun phrase
is “Wisniewski”
ends in “-ski”
Lots of learning systems are not confounded by multiple, non-independent features: decision trees, neural nets, SVMs, …
What is a symbol?
St -1
St
Ot
St+1
Ot +1
Ot -1
identity of wordends in “-ski”is capitalizedis part of a noun phraseis in a list of city namesis under node X in WordNetis in bold fontis indentedis in hyperlink anchor…
…
…part of
noun phrase
is “Wisniewski”
ends in “-ski”
Idea: replace generative model in HMM with a maxent model, where state depends on observations
...)|Pr( tt xs
What is a symbol?
St -1
St
Ot
St+1
Ot +1
Ot -1
identity of wordends in “-ski”is capitalizedis part of a noun phraseis in a list of city namesis under node X in WordNetis in bold fontis indentedis in hyperlink anchor…
…
…part of
noun phrase
is “Wisniewski”
ends in “-ski”
Idea: replace generative model in HMM with a maxent model, where state depends on observations and previous state
...),|Pr( ,1 ttt sxs
What is a symbol?
St -1 S
t
Ot
St+1
Ot +1
Ot -1
identity of wordends in “-ski”is capitalizedis part of a noun phraseis in a list of city namesis under node X in WordNetis in bold fontis indentedis in hyperlink anchor…
…
…part of
noun phrase
is “Wisniewski”
ends in “-ski”
Idea: replace generative model in HMM with a maxent model, where state depends on observations and previous state history
......),|Pr( ,2,1 tttt ssxs
Ratnaparkhi’s MXPOST
• Sequential learning problem: predict POS tags of words.
• Uses MaxEnt model described above.
• Rich feature set.• To smooth, discard features
occurring < 10 times.
Conditional Markov Models (CMMs) aka MEMMs aka Maxent Taggers vs HMMS
St-1 St
Ot
St+1
Ot+1Ot-1
...
i
iiii sossos )|Pr()|Pr(),Pr( 11
St-1 St
Ot
St+1
Ot+1Ot-1
...
i
iii ossos ),|Pr()|Pr( 11
Label Bias Problem
• Consider this MEMM, and enough training data to perfectly model it:
Pr(0123|rob) = Pr(1|0,r)/Z1 * Pr(2|1,o)/Z2 * Pr(3|2,b)/Z3= 0.5 * 1 * 1
Pr(0453|rib) = Pr(4|0,r)/Z1’ * Pr(5|4,i)/Z2’ * Pr(3|5,b)/Z3’= 0.5 * 1 *1
Pr(0123|rib)=1
Pr(0453|rob)=1
Another view of label bias [Sha & Pereira]
So what’s the alternative?
CMMs to CRFs
j j
ijjjii
jjjjnn xZ
yyxfxyyxxyy
)(
)),,(exp()|Pr()...|...Pr(
1
,111
)(
)),(exp(
xZ
yxFi
ii
New model
j
jjjii
jj
iii
yyxfyxFxZ
yxF),,(),( where,
)(
)),(exp(
1
What’s the new model look like?
)(
),,(exp(
)(
)),(exp( 1
xZ
yyxf
xZ
yxFi j
jjjii
iii
x1 x2 x3
y1 y2 y3
What’s independent?
Graphical comparison among HMMs, MEMMs and CRFs
HMM MEMM CRF
Sha & Pereira results
CRF beats MEMM (McNemar’s test); MEMM probably beats voted perceptron
Sha & Pereira results
in minutes, 375k examples
Broader Issues in IE
Broader View
Create ontology
SegmentClassifyAssociateCluster
Load DB
Spider
Query,Search
Data mine
IE
Documentcollection
Database
Filter by relevance
Label training data
Train extraction models
Up to now we have been focused on segmentation and classification
Broader View
Create ontology
SegmentClassifyAssociateCluster
Load DB
Spider
Query,Search
Data mine
IETokenize
Documentcollection
Database
Filter by relevance
Label training data
Train extraction models
Now touch on some other issues
12
3
4
5
(1) Association as Binary Classification
[Zelenko et al, 2002]
Christos Faloutsos conferred with Ted Senator, the KDD 2003 General Chair.
Person-Role (Christos Faloutsos, KDD 2003 General Chair) NO
Person-Role ( Ted Senator, KDD 2003 General Chair) YES
Person Person Role
Do this with SVMs and tree kernels over parse trees.
(1) Association with Finite State Machines[Ray & Craven, 2001]
… This enzyme, UBC6, localizes to the endoplasmic reticulum, with the catalytic domain facing the cytosol. …
DET thisN enzymeN ubc6V localizesPREP toART theADJ endoplasmicN reticulumPREP withART theADJ catalyticN domainV facingART theN cytosol Subcellular-localization (UBC6, endoplasmic reticulum)
(1) Association with Graphical Models[Roth & Yih 2002]Capture arbitrary-distance
dependencies among predictions.
Local languagemodels contributeevidence to entityclassification.
Local languagemodels contributeevidence to relationclassification.
Random variableover the class ofentity #2, e.g. over{person, location,…}
Random variableover the class ofrelation between entity #2 and #1, e.g. over {lives-in, is-boss-of,…}
Dependencies between classesof entities and relations!
Inference with loopy belief propagation.
(1) Association with Graphical Models[Roth & Yih 2002]Also capture long-distance
dependencies among predictions.
Local languagemodels contributeevidence to entityclassification.
Random variableover the class ofentity #1, e.g. over{person, location,…}
Local languagemodels contributeevidence to relationclassification.
Random variableover the class ofrelation between entity #2 and #1, e.g. over {lives-in, is-boss-of,…}
Dependencies between classesof entities and relations!
Inference with loopy belief propagation.
location
personlives-in
Broader View
Create ontology
SegmentClassifyAssociateCluster
Load DB
Spider
Query,Search
Data mine
IETokenize
Documentcollection
Database
Filter by relevance
Label training data
Train extraction models
Now touch on some other issues
12
3
4
5
When do two extracted stringsrefer to the same object?
(2) Information Integration
Goal might be to merge results of two IE systems:
Name: Introduction to Computer Science
Number: CS 101
Teacher: M. A. Kludge
Time: 9-11am
Name: Data Structures in Java
Room: 5032 Wean Hall
Title: Intro. to Comp. Sci.
Num: 101
Dept: Computer Science
Teacher: Dr. Klüdge
TA: John Smith
Topic: Java Programming
Start time: 9:10 AM
[Minton, Knoblock, et al 2001], [Doan, Domingos, Halevy 2001],[Richardson & Domingos 2003]
(2) Other Information Integration Issues
• Distance metrics for text – which work well?– [Cohen, Ravikumar, Fienberg, 2003]
• Finessing integration by soft database operations based on similarity – [Cohen, 2000]
• Integration of complex structured databases: (capture dependencies among multiple merges)– [Cohen, MacAllister, Kautz KDD 2000; Pasula,
Marthi, Milch, Russell, Shpitser, NIPS 2002; McCallum and Wellner, KDD WS 2003]
IE Resources
• Data– RISE, http://www.isi.edu/~muslea/RISE/index.html– Linguistic Data Consortium (LDC)
• Penn Treebank, Named Entities, Relations, etc.– http://www.biostat.wisc.edu/~craven/ie
• Papers, tutorials, lectures, code– http://www.cs.cmu.edu/~wcohen/10-707
References• [Bikel et al 1997] Bikel, D.; Miller, S.; Schwartz, R.; and Weischedel, R. Nymble: a high-performance learning name-finder. In
Proceedings of ANLP’97, p194-201.• [Califf & Mooney 1999], Califf, M.E.; Mooney, R.: Relational Learning of Pattern-Match Rules for Information Extraction, in
Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99).• [Cohen, Hurst, Jensen, 2002] Cohen, W.; Hurst, M.; Jensen, L.: A flexible learning system for wrapping tables and lists in HTML
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