Information Extraction, Conditional Random Fields, and Social Network Analysis

Information Extraction,Conditional Random Fields,and Social Network Analysis

Andrew McCallum

Computer Science Department

University of Massachusetts Amherst

Joint work with

Aron Culotta, Charles Sutton, Ben Wellner, Khashayar Rohanimanesh, Wei Li,Andres Corrada, Xuerui Wang

Goal:

Mine actionable knowledgefrom unstructured text.

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

A Portal for Job Openings

Job

Op

enin

gs:

Cat

ego

ry =

Hig

h T

ech

Key

wo

rd =

Jav

a L

oca

tio

n =

U.S

.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Data Mining the Extracted Job Information

IE fromChinese Documents regarding Weather

Department of Terrestrial System, Chinese Academy of Sciences

200k+ documentsseveral millennia old

- Qing Dynasty Archives- memos- newspaper articles- diaries

IE from Cargo Container Ship Manifests

Cargo Tracking Div.US Navy

http://images.google.com/imgres?imgurl=www.setv.org/jpgs/usnavy_seal.gif&imgrefurl=http://www.setv.org/mil_stmnts.html&h=140&w=142&prev=/images%3Fq%3Dus%2Bnavy%2Bseal%26start%3D40%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26oe%3DUTF-8%26sa%3DN

IE from Research Papers[McCallum et al ‘99]

IE from Research Papers

Mining Research Papers





[Giles et al]

[Rosen-Zvi, Griffiths, Steyvers, Smyth, 2004]

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


Information Extraction = segmentation + classification + association + clustering


October 14, 2002, 4:00 a.m. PT









October 14, 2002, 4:00 a.m. PT









October 14, 2002, 4:00 a.m. PT






NAME

TITLE ORGANIZATION

Bill Gates

CEO

Microsoft

Bill Veghte

VP

Microsoft

Richard Stallman

founder

Free Soft..

*

*

*

*

Larger Context

SegmentClassifyAssociateCluster

Filter

Prediction Outlier detection Decision support

IE

Documentcollection

Database

Discover patterns - entity types - links / relations - events

DataMining

Spider

Actionableknowledge

Outline

• Examples of IE and Data Mining.

• Conditional Random Fields and Feature Induction.

• Joint inference: Motivation and examples

– Joint Labeling of Cascaded Sequences (Belief Propagation)

– Joint Labeling of Distant Entities (BP by Tree Reparameterization)

– Joint Co-reference Resolution (Graph Partitioning)

– Joint Segmentation and Co-ref (Iterated Conditional Samples.)

• Two example projects

– Email, contact address book, and Social Network Analysis

– Research Paper search and analysis

Hidden Markov Models

St - 1

St

Ot

St+1

Ot +1

Ot -1

...

...

Finite state model Graphical model

Parameters: for all states S={s1,s2,…} Start state probabilities: P(st ) Transition probabilities: P(st|st-1 ) Observation (emission) probabilities: P(ot|st )Training: Maximize probability of training observations (w/ prior)

∏=

−∝||

11 )|()|(),(

o

ttttt soPssPosP

vvv

HMMs are the standard sequence modeling tool in genomics, music, speech, NLP, …

...transitions

observations

o1 o2 o3 o4 o5 o6 o7 o8

Generates:

State sequenceObservation sequence

Usually a multinomial over atomic, fixed alphabet

IE with Hidden Markov Models

Yesterday Rich Caruana spoke this example sentence.

Yesterday Rich Caruana spoke this example sentence.

Person name: Rich Caruana

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:

person name

location name

background

We want More than an Atomic View of Words

Would like richer representation of text: many arbitrary, overlapping features of the 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 anchorlast person name was femalenext two words are “and Associates”

…

…part of

noun phrase

is “Wisniewski”

ends in “-ski”

Problems with Richer Representationand a Joint Model

These arbitrary features are not independent.– Multiple levels of granularity (chars, words, phrases)

– Multiple dependent modalities (words, formatting, layout)

– Past & future

Two choices:

Model the dependencies.Each state would have its own Bayes Net. But we are already starved for training data!

Ignore the dependencies.This causes “over-counting” of evidence (ala naïve Bayes). Big problem when combining evidence, as in Viterbi!

St - 1

St

Ot

St+1

Ot +1

Ot -1

St - 1

St

Ot

St+1

Ot +1

Ot -1

Conditional Sequence Models

• We prefer a model that is trained to maximize a conditional probability rather than joint probability:P(s|o) instead of P(s,o):

– Can examine features, but not responsible for generating them.

– Don’t have to explicitly model their dependencies.

– Don’t “waste modeling effort” trying to generate what we are given at test time anyway.

€

vs = s1,s2,...sn

v o = o1,o2,...on

Joint

Conditional

St-1 St

Ot

St+1

Ot+1Ot-1

St-1 St

Ot

St+1

Ot+1Ot-1

...

...

...

...

€

P(v s ,

v o ) = P(st | st−1)P(ot | st )

t=1

|v o |

∏

€

Φo(t) = exp λ k fk (st ,ot )k

∑ ⎛

⎝ ⎜

⎞

⎠ ⎟ (A super-special case of

Conditional Random Fields.)

[Lafferty, McCallum, Pereira 2001]

€

P(v s |

v o ) =

1

P(v o )

P(st | st−1)P(ot | st )t=1

|v o |

∏

€

=1

Z(v o )

Φs(st ,st−1)Φo(ot ,st )t=1

|v o |

∏

where

From HMMs to Conditional Random Fields

Set parameters by maximum likelihood, using optimization method on L.

Conditional Random Fields

St St+1 St+2

O = Ot, Ot+1, Ot+2, Ot+3, Ot+4

St+3 St+4

1. FSM special-case: linear chain among unknowns, parameters tied across time steps.

∏ ∑=

− ⎟⎠

⎞⎜⎝

⎛=

||

11 ),,,(exp

)(

1)|(

o

t kttkk tossf

oZosP

vv

vvv

λ

[Lafferty, McCallum, Pereira 2001]

2. In general: CRFs = "Conditionally-trained Markov Network" arbitrary structure among unknowns

3. Relational Markov Networks [Taskar, Abbeel, Koller 2002]: Parameters tied across hits from SQL-like queries ("clique templates")

Training CRFs

€

Log - likelihood gradient :

∂L

∂λ k

= Ck (v s ( i),

v o (i))

i

∑ − P{λk }(v s |

v o (i)) Ck (

v s ,

v o ( i))

v s

∑i

∑ − λ k2

Ck (v s ,

v o ) = fk (

t

∑ v o , t,st−1,st )

€

Maximize log - likelihood of parameters given training data :

L({λ k} |{v o ,

v s

( i)})

Feature count using correct labels

Feature count using predicted labels

Smoothing penalty- -

Linear-chain CRFs vs. HMMs

• Comparable computational efficiency for inference

• Features may be arbitrary functions of any or all observations

– Parameters need not fully specify generation of observations; can require less training data

– Easy to incorporate domain knowledge

IE from Research Papers[McCallum et al ‘99]

IE from Research Papers

Field-level F1

Hidden Markov Models (HMMs) 75.6[Seymore, McCallum, Rosenfeld, 1999]

Support Vector Machines (SVMs) 89.7[Han, Giles, et al, 2003]

Conditional Random Fields (CRFs) 93.9[Peng, McCallum, 2004]

error40%

Table Extraction from Government ReportsCash receipts from marketings of milk during 1995 at $19.9 billion dollars, was slightly below 1994. Producer returns averaged $12.93 per hundredweight, $0.19 per hundredweight below 1994. Marketings totaled 154 billion pounds, 1 percent above 1994. Marketings include whole milk sold to plants and dealers as well as milk sold directly to consumers. An estimated 1.56 billion pounds of milk were used on farms where produced, 8 percent less than 1994. Calves were fed 78 percent of this milk with the remainder consumed in producer households. Milk Cows and Production of Milk and Milkfat: United States, 1993-95 -------------------------------------------------------------------------------- : : Production of Milk and Milkfat 2/ : Number :------------------------------------------------------- Year : of : Per Milk Cow : Percentage : Total :Milk Cows 1/:-------------------: of Fat in All :------------------ : : Milk : Milkfat : Milk Produced : Milk : Milkfat -------------------------------------------------------------------------------- : 1,000 Head --- Pounds --- Percent Million Pounds : 1993 : 9,589 15,704 575 3.66 150,582 5,514.4 1994 : 9,500 16,175 592 3.66 153,664 5,623.7 1995 : 9,461 16,451 602 3.66 155,644 5,694.3 --------------------------------------------------------------------------------1/ Average number during year, excluding heifers not yet fresh. 2/ Excludes milk sucked by calves.

Table Extraction from Government Reports

Cash receipts from marketings of milk during 1995 at $19.9 billion dollars, was

slightly below 1994. Producer returns averaged $12.93 per hundredweight,

$0.19 per hundredweight below 1994. Marketings totaled 154 billion pounds,

1 percent above 1994. Marketings include whole milk sold to plants and dealers

as well as milk sold directly to consumers.

An estimated 1.56 billion pounds of milk were used on farms where produced,

8 percent less than 1994. Calves were fed 78 percent of this milk with the

remainder consumed in producer households.

Milk Cows and Production of Milk and Milkfat:

United States, 1993-95

--------------------------------------------------------------------------------

: : Production of Milk and Milkfat 2/

: Number :-------------------------------------------------------

Year : of : Per Milk Cow : Percentage : Total

:Milk Cows 1/:-------------------: of Fat in All :------------------

: : Milk : Milkfat : Milk Produced : Milk : Milkfat

--------------------------------------------------------------------------------

: 1,000 Head --- Pounds --- Percent Million Pounds

:

1993 : 9,589 15,704 575 3.66 150,582 5,514.4

1994 : 9,500 16,175 592 3.66 153,664 5,623.7

1995 : 9,461 16,451 602 3.66 155,644 5,694.3

--------------------------------------------------------------------------------

1/ Average number during year, excluding heifers not yet fresh.

2/ Excludes milk sucked by calves.

CRFLabels:• Non-Table• Table Title• Table Header• Table Data Row• Table Section Data Row• Table Footnote• ... (12 in all)

[Pinto, McCallum, Wei, Croft, 2003 SIGIR]

Features:• Percentage of digit chars• Percentage of alpha chars• Indented• Contains 5+ consecutive spaces• Whitespace in this line aligns with prev.• ...• Conjunctions of all previous features,

time offset: {0,0}, {-1,0}, {0,1}, {1,2}.

100+ documents from www.fedstats.gov

Table Extraction Experimental Results

Line labels,percent correct

Table segments,F1

95 % 92 %

65 % 64 %

85 % -

HMM

StatelessMaxEnt

CRF w/outconjunctions

CRF

52 % 68 %

[Pinto, McCallum, Wei, Croft, 2003 SIGIR]

Feature Induction for CRFs

1. Begin with knowledge of atomic features, but no features yet in the model.

2. Consider many candidate features, including atomic and conjunctions.

3. Evaluate each candidate feature.

4. Add to the model some that are ranked highest.

5. Train the model.

[McCallum, 2003, UAI]

Candidate Feature Evaluation

Common method: Information Gain

∑∈

−=Ff

fCHfPCHFC )|( )()(),(InfoGain

True optimization criterion: Likelihood of training data

Λ+Λ −= LLf fλλ ),(Gain Likelihood

Technical meat is in how to calculate this efficiently for CRFs• Mean field approximation• Emphasize error instances (related to Boosting)• Newton's method to set

[McCallum, 2003, UAI]

Named Entity Recognition

CRICKET - MILLNS SIGNS FOR BOLAND

CAPE TOWN 1996-08-22

South African provincial side Boland said on Thursday they had signed Leicestershire fast bowler David Millns on a one year contract. Millns, who toured Australia with England A in 1992, replaces former England all-rounder Phillip DeFreitas as Boland's overseas professional.

Labels: Examples:

PER Yayuk BasukiInnocent Butare

ORG 3MKDPCleveland

LOC ClevelandNirmal HridayThe Oval

MISC JavaBasque1,000 Lakes Rally

Automatically Induced Features

Index Feature

0 inside-noun-phrase (ot-1)

5 stopword (ot)

20 capitalized (ot+1)

75 word=the (ot)

100 in-person-lexicon (ot-1)

200 word=in (ot+2)

500 word=Republic (ot+1)

711 word=RBI (ot) & header=BASEBALL

1027 header=CRICKET (ot) & in-English-county-lexicon (ot)

1298 company-suffix-word (firstmentiont+2)

4040 location (ot) & POS=NNP (ot) & capitalized (ot) & stopword (ot-1)

4945 moderately-rare-first-name (ot-1) & very-common-last-name (ot)

4474 word=the (ot-2) & word=of (ot)

[McCallum & Li, 2003, CoNLL]

Named Entity Extraction Results

Method F1

HMMs BBN's Identifinder 73%

CRFs w/out Feature Induction 83%

CRFs with Feature Induction 90%based on LikelihoodGain

[McCallum & Li, 2003, CoNLL]

Outline











Problem:

Combined in serial juxtaposition,IE and KD are unaware of each others’ weaknesses and opportunities.

1) KD begins from a populated DB, unaware of where the data came from, or its inherent uncertainties.

2) IE is unaware of emerging patterns and regularities in the DB.

The accuracy of both suffers, and significant mining of complex text sources is beyond reach.


IE

Documentcollection

Database


KnowledgeDiscovery

Actionableknowledge


Filter


IE

Documentcollection

Database


DataMining

Spider

Actionableknowledge

Uncertainty Info

Emerging Patterns

Solution:


Filter


IE

Documentcollection

ProbabilisticModel


DataMining

Spider

Actionableknowledge

Solution:

Conditional Random Fields [Lafferty, McCallum, Pereira]

Conditional PRMs [Koller…], [Jensen…], [Geetor…], [Domingos…]

Discriminatively-trained undirected graphical models

Complex Inference and LearningJust what we researchers like to sink our teeth into!

Unified Model

1. Jointly labeling cascaded sequencesFactorial CRFs

Part-of-speech

Noun-phrase boundaries

Named-entity tag

English words

[Sutton, Khashayar, McCallum, ICML 2004]


Part-of-speech


Named-entity tag

English words



Part-of-speech


Named-entity tag

English words


But errors cascade--must be perfect at every stage to do well.


Part-of-speech


Named-entity tag

English words


Joint prediction of part-of-speech and noun-phrase in newswire,matching accuracy with only 50% of the training data.

Inference:Tree reparameterization BP

[Wainwright et al, 2002]

2. Jointly labeling distant mentionsSkip-chain CRFs

Senator Joe Green said today … . Green ran for …

…

[Sutton, McCallum, SRL 2004]

Dependency among similar, distant mentions ignored.

2. Jointly labeling distant mentionsSkip-chain CRFs

Senator Joe Green said today … . Green ran for …

…

[Sutton, McCallum, SRL 2004]

14% reduction in error on most repeated field in email seminar announcements.

Inference:Tree reparameterization BP

[Wainwright et al, 2002]

3. Joint co-reference among all pairsAffinity Matrix CRF

. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

45

99Y/N

Y/N

Y/N

11

[McCallum, Wellner, IJCAI WS 2003, NIPS 2004]

~25% reduction in error on co-reference ofproper nouns in newswire.

Inference:Correlational clusteringgraph partitioning

[Bansal, Blum, Chawla, 2002]

“Entity resolution”“Object correspondence”

Coreference Resolution

Input

AKA "record linkage", "database record deduplication", "entity resolution", "object correspondence", "identity uncertainty"

Output

News article, with named-entity "mentions" tagged

Number of entities, N = 3

#1 Secretary of State Colin Powell he Mr. Powell Powell

#2 Condoleezza Rice she Rice

#3 President Bush Bush

Today Secretary of State Colin Powell met with . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . he . . . . . .. . . . . . . . . . . . . Condoleezza Rice . . . . .. . . . Mr Powell . . . . . . . . . .she . . . . . . . . . . . . . . . . . . . . . Powell . . . . . . . . . . . .. . . President Bush . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . Rice . . . . . . . . . . . . . . . . Bush . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

Inside the Traditional Solution

Mention (3) Mention (4)

. . . Mr Powell . . . . . . Powell . . .

N Two words in common 29Y One word in common 13Y "Normalized" mentions are string identical 39Y Capitalized word in common 17Y > 50% character tri-gram overlap 19N < 25% character tri-gram overlap -34Y In same sentence 9Y Within two sentences 8N Further than 3 sentences apart -1Y "Hobbs Distance" < 3 11N Number of entities in between two mentions = 0 12N Number of entities in between two mentions > 4 -3Y Font matches 1Y Default -19

OVERALL SCORE = 98 > threshold=0

Pair-wise Affinity Metric

Y/N?

The Problem

. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

affinity = 98

affinity = 11

affinity = 104

Pair-wise mergingdecisions are beingmade independentlyfrom each otherY

Y

N

Affinity measures are noisy and imperfect.

They should be madein relational dependencewith each other.

A Markov Random Field for Co-reference

. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

45

30Y/N

Y/N

Y/N

[McCallum & Wellner, 2003, ICML](MRF)

Make pair-wise mergingdecisions in dependent relation to each other by- calculating a joint prob.- including all edge weights- adding dependence on consistent triangles.

11

€

P(v y |

v x ) =

1

Z v x

exp λ l f l (x i, x j , y ij ) + λ ' f '(y ij , y jk,y ik )i, j,k

∑l

∑i, j

∑ ⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟


. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

45

30Y/N

Y/N

Y/N

[McCallum & Wellner, 2003](MRF)

Make pair-wise mergingdecisions in dependent relation to each other by- calculating a joint prob.- including all edge weights- adding dependence on consistent triangles.

11 ∞−

€

P(v y |

v x ) =

1

Z v x


∑l

∑i, j

∑ ⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟


. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

Y

Y

N

[McCallum & Wellner, 2003]

€

P(v y |

v x ) =

1

Z v x


∑l

∑i, j

∑ ⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟

(MRF)

infinity

45)

30)

(11)


. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

N

Y

N


€

P(v y |

v x ) =

1

Z v x


∑l

∑i, j

∑ ⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟

(MRF)

64

45)

30)

(11)

Inference in these MRFs = Graph Partitioning[Boykov, Vekler, Zabih, 1999], [Kolmogorov & Zabih, 2002], [Yu, Cross, Shi, 2002]

. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

45

11

30

. . . Condoleezza Rice . . .

134

10

€

log P(v y |

v x )( )∝ λ l f l (x i,x j , y ij )

l

∑i, j

∑ = w ij

i, j w/inparitions

∑ − w ij

i, j acrossparitions

∑

106


. . . Mr Powell . . .

. . . Powell . . .

. . . she . . . . . . Condoleezza Rice . . .

= 22

45

11

30

134

10

106

€

log P(v y |

v x )( )∝ λ l f l (x i,x j , y ij )

l

∑i, j

∑ = w ij

i, j w/inparitions

∑ − w ij

i, j acrossparitions

∑


. . . Mr Powell . . .

. . . Powell . . .

. . . she . . . . . . Condoleezza Rice . . .

€

log P(v y |

v x )( )∝ λ l f l (x i, x j , y ij )

l

∑i, j

∑ = w ij

i, j w/inparitions

∑ + w'iji, j acrossparitions

∑ = 314

45

11

30

134

10

106

Co-reference Experimental Results

Proper noun co-reference

DARPA ACE broadcast news transcripts, 117 stories

Partition F1 Pair F1Single-link threshold 16 % 18 %Best prev match [Morton] 83 % 89 %MRFs 88 % 92 %

error=30% error=28%

DARPA MUC-6 newswire article corpus, 30 stories

Partition F1 Pair F1Single-link threshold 11% 7 %Best prev match [Morton] 70 % 76 %MRFs 74 % 80 %

error=13% error=17%


Joint co-reference among all pairsAffinity Matrix CRF

. . . Mr Powell . . .

. . . Powell . . .

. . . she . . .

45

99Y/N

Y/N

Y/N

11

[McCallum, Wellner, IJCAI WS 2003, NIPS 2004]

~25% reduction in error on co-reference ofproper nouns in newswire.

Inference:Correlational clusteringgraph partitioning

[Bansal, Blum, Chawla, 2002]

p

Databasefield values

c

4. Joint segmentation and co-reference

o

s

o

s

c

c

s

o

Citation attributes

y y

y

Segmentation

[Wellner, McCallum, Peng, Hay, UAI 2004]Inference:Variant of Iterated Conditional Modes

Co-reference decisions

Laurel, B. Interface Agents: Metaphors with Character, in The Art of Human-Computer Interface Design, B. Laurel (ed), Addison-Wesley, 1990.

Brenda Laurel. Interface Agents: Metaphors with Character, in Laurel, The Art of Human-Computer Interface Design, 355-366, 1990.

[Besag, 1986]

World Knowledge

35% reduction in co-reference error by using segmentation uncertainty.

6-14% reduction in segmentation error by using co-reference.

Extraction from and matching of research paper citations.

see also [Marthi, Milch, Russell, 2003]

Joint IE and Coreference from Research Paper Citations

Textual citation mentions(noisy, with duplicates)

Paper database, with fields,clean, duplicates collapsed



AUTHORS TITLE VENUECowell, Dawid… Probab… SpringerMontemerlo, Thrun…FastSLAM… AAAI…Kjaerulff Approxi… Technic…


Laurel, B. Interface Agents: Metaphors with Character , in

The Art of Human-Computer Interface Design , T. Smith (ed) ,

Addison-Wesley , 1990 .

Brenda Laurel . Interface Agents: Metaphors with Character , in

Smith , The Art of Human-Computr Interface Design , 355-366 , 1990 .

Citation Segmentation and Coreference






1) Segment citation fields








2) Resolve coreferent citations


Y?N








3) Form canonical database record


AUTHOR = Brenda Laurel TITLE = Interface Agents: Metaphors with CharacterPAGES = 355-366BOOKTITLE = The Art of Human-Computer Interface DesignEDITOR = T. SmithPUBLISHER = Addison-WesleyYEAR = 1990

Y?N

Resolving conflicts








3) Form canonical database record


AUTHOR = Brenda Laurel TITLE = Interface Agents: Metaphors with CharacterPAGES = 355-366BOOKTITLE = The Art of Human-Computer Interface DesignEDITOR = T. SmithPUBLISHER = Addison-WesleyYEAR = 1990

Y?N

Perform jointly.

x

s

Observed citation

CRF Segmentation

IE + Coreference Model

J Besag 1986 On the…

AUT AUT YR TITL TITL

x

s

Observed citation

CRF Segmentation


Citation mention attributes

J Besag 1986 On the…

AUTHOR = “J Besag”YEAR = “1986”TITLE = “On the…”

c

x

s


c

J Besag 1986 On the…Smyth . 2001 Data Mining…

Smyth , P Data mining…

Structure for each citation mention

x

s


c

Binary coreference variablesfor each pair of mentions



x

s


c

y n

n



Binary coreference variablesfor each pair of mentions

y n

n

x

s


c



Research paper entity attribute nodes

AUTHOR = “P Smyth”YEAR = “2001”TITLE = “Data Mining…”...

p

Databasefield values

c


o

s

o

s

c

c

s

o

Citation attributes

y y

y

Segmentation

[Wellner, McCallum, Peng, Hay, UAI 2004]

Inference:Variant of Iterated Conditional Modes

Co-reference decisions

Laurel, B. Interface Agents: Metaphors with Character, in The Art of Human-Computer Interface Design, B. Laurel (ed), Addison-Wesley, 1990.

Brenda Laurel. Interface Agents: Metaphors with Character, in Laurel, The Art of Human-Computer Interface Design, 355-366, 1990.

[Besag, 1986]

World Knowledge

35% reduction in co-reference error by using segmentation uncertainty.

6-14% reduction in segmentation error by using co-reference.

Extraction from and matching of research paper citations.

Outline











Workplace effectiveness ~ Ability to leverage network of acquaintances

But filling Contacts DB by hand is tedious, and incomplete.

QuickTime™ and aTIFF (Uncompressed) decompressor


QuickTime™ and aTIFF (Uncompressed) decompressor


Email Inbox Contacts DB

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

WWW

Automatically

Managing and UnderstandingConnections of People in our Email World

System Overview

ContactInfo andPerson Name

Extraction

Person Name

Extraction

NameCoreference

HomepageRetrieval

Social NetworkAnalysis

KeywordExtraction

CRFWWW

names

Email QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

An ExampleTo: “Andrew McCallum” [email protected]

Subject ...

First Name:

Andrew

Middle Name:

Kachites

Last Name:

McCallum

JobTitle: Associate Professor

Company: University of Massachusetts

Street Address:

140 Governor’s Dr.

City: Amherst

State: MA

Zip: 01003

Company Phone:

(413) 545-1323

Links: Fernando Pereira, Sam Roweis,…

Key Words:

Information extraction,

social network,…

Search for new people

Summary of Results

Token

Acc

Field

Prec

Field

Recall

Field

F1

CRF 94.50 85.73 76.33 80.76

Person Keywords

William Cohen Logic programming

Text categorization

Data integration

Rule learning

Daphne Koller Bayesian networks

Relational models

Probabilistic models

Hidden variables

Deborah McGuiness

Semantic web

Description logics

Knowledge representation

Ontologies

Tom Mitchell Machine learning

Cognitive states

Learning apprentice

Artificial intelligence

Contact info and name extraction performance (25 fields)

Example keywords extracted

1. Expert Finding: When solving some task, find friends-of-friends with relevant expertise. Avoid “stove-piping” in large org’s by automatically suggesting collaborators. Given a task, automatically suggest the right team for the job. (Hiring aid!)

2. Social Network Analysis: Understand the social structure of your organization. Suggest structural changes for improved efficiency.



Clustering words into topics withLatent Dirichlet Allocation

[Blei, Ng, Jordan 2003]

STORYSTORIES

TELLCHARACTER

CHARACTERSAUTHOR

READTOLD

SETTINGTALESPLOT

TELLINGSHORT

FICTIONACTION

TRUEEVENTSTELLSTALE

NOVEL

MINDWORLDDREAM

DREAMSTHOUGHT

IMAGINATIONMOMENT

THOUGHTSOWNREALLIFE

IMAGINESENSE

CONSCIOUSNESSSTRANGEFEELINGWHOLEBEINGMIGHTHOPE

WATERFISHSEA

SWIMSWIMMING

POOLLIKE

SHELLSHARKTANK

SHELLSSHARKSDIVING

DOLPHINSSWAMLONGSEALDIVE

DOLPHINUNDERWATER

DISEASEBACTERIADISEASES

GERMSFEVERCAUSE

CAUSEDSPREADVIRUSES

INFECTIONVIRUS

MICROORGANISMSPERSON

INFECTIOUSCOMMONCAUSING

SMALLPOXBODY

INFECTIONSCERTAIN

Example topicsinduced from a large collection of text

FIELDMAGNETIC

MAGNETWIRE

NEEDLECURRENT

COILPOLESIRON

COMPASSLINESCORE

ELECTRICDIRECTION

FORCEMAGNETS

BEMAGNETISM

POLEINDUCED

SCIENCESTUDY

SCIENTISTSSCIENTIFIC

KNOWLEDGEWORK

RESEARCHCHEMISTRY

TECHNOLOGYMANY

MATHEMATICSBIOLOGY

FIELDPHYSICS

LABORATORYSTUDIESWORLD

SCIENTISTSTUDYINGSCIENCES

BALLGAMETEAM

FOOTBALLBASEBALLPLAYERS

PLAYFIELD

PLAYERBASKETBALL

COACHPLAYEDPLAYING

HITTENNISTEAMSGAMESSPORTS

BATTERRY

JOBWORKJOBS

CAREEREXPERIENCE

EMPLOYMENTOPPORTUNITIES

WORKINGTRAINING

SKILLSCAREERS

POSITIONSFIND

POSITIONFIELD

OCCUPATIONSREQUIRE

OPPORTUNITYEARNABLE

[Tennenbaum et al]

STORYSTORIES

TELLCHARACTER

CHARACTERSAUTHOR

READTOLD

SETTINGTALESPLOT

TELLINGSHORT

FICTIONACTION

TRUEEVENTSTELLSTALE

NOVEL

MINDWORLDDREAM

DREAMSTHOUGHT

IMAGINATIONMOMENT

THOUGHTSOWNREALLIFE

IMAGINESENSE

CONSCIOUSNESSSTRANGEFEELINGWHOLEBEINGMIGHTHOPE

WATERFISHSEA

SWIMSWIMMING

POOLLIKE

SHELLSHARKTANK

SHELLSSHARKSDIVING

DOLPHINSSWAMLONGSEALDIVE

DOLPHINUNDERWATER

DISEASEBACTERIADISEASES

GERMSFEVERCAUSE

CAUSEDSPREADVIRUSES

INFECTIONVIRUS

MICROORGANISMSPERSON

INFECTIOUSCOMMONCAUSING

SMALLPOXBODY

INFECTIONSCERTAIN

FIELDMAGNETIC

MAGNETWIRE

NEEDLECURRENT

COILPOLESIRON

COMPASSLINESCORE

ELECTRICDIRECTION

FORCEMAGNETS

BEMAGNETISM

POLEINDUCED

SCIENCESTUDY

SCIENTISTSSCIENTIFIC

KNOWLEDGEWORK

RESEARCHCHEMISTRY

TECHNOLOGYMANY

MATHEMATICSBIOLOGY

FIELDPHYSICS

LABORATORYSTUDIESWORLD

SCIENTISTSTUDYINGSCIENCES

BALLGAMETEAM

FOOTBALLBASEBALLPLAYERS

PLAYFIELD

PLAYERBASKETBALL

COACHPLAYEDPLAYING

HITTENNISTEAMSGAMESSPORTS

BATTERRY

JOBWORKJOBS

CAREEREXPERIENCE

EMPLOYMENTOPPORTUNITIES

WORKINGTRAINING

SKILLSCAREERS

POSITIONSFIND

POSITIONFIELD

OCCUPATIONSREQUIRE

OPPORTUNITYEARNABLE

Example topicsinduced from a large collection of text

[Tennenbaum et al]

From LDA to Author-Recipient-Topic(ART)

Inference and Estimation

Gibbs Sampling:- Easy to implement- Reasonably fast

r

Enron Email Corpus

• 250k email messages• 23k people

Date: Wed, 11 Apr 2001 06:56:00 -0700 (PDT)From: [email protected]: [email protected]: Enron/TransAltaContract dated Jan 1, 2001

Please see below. Katalin Kiss of TransAlta has requested an electronic copy of our final draft? Are you OK with this? If so, the only version I have is the original draft without revisions.

DP

Debra PerlingiereEnron North America Corp.Legal Department1400 Smith Street, EB 3885Houston, Texas [email protected]

Topics, and prominent sender/receiversdiscovered by ART

Topics, and prominent sender/receiversdiscovered by ART

Beck = “Chief Operations Officer”Dasovich = “Government Relations Executive”Shapiro = “Vice Presidence of Regulatory Affairs”Steffes = “Vice President of Government Affairs”

Comparing Role Discovery

connection strength (A,B) =

distribution overauthored topics

Traditional SNA

distribution overrecipients

distribution overauthored topics

Author-TopicART

Comparing Role Discovery Tracy Geaconne Dan McCarty

Traditional SNA Author-TopicART

Similar roles Different rolesDifferent roles

Geaconne = “Secretary”McCarty = “Vice President”


Different roles Very similarNot very similar

Geaconne = “Secretary”Hayslett = “Vice President & CTO”

Comparing Role Discovery Tracy Geaconne Rod Hayslett


Different roles Very differentVery similar

Blair = “Gas pipeline logistics”Watson = “Pipeline facilities planning”

Comparing Role Discovery Lynn Blair Kimberly Watson


Block structured NotNot

Comparing Group Discovery Enron TransWestern Division

McCallum Email Corpus 2004

• January - October 2004• 23k email messages• 825 people

From: [email protected]: NIPS and ....Date: June 14, 2004 2:27:41 PM EDTTo: [email protected]

There is pertinent stuff on the first yellow folder that is completed either travel or other things, so please sign that first folder anyway. Then, here is the reminder of the things I'm still waiting for:

NIPS registration receipt.CALO registration receipt.

Thanks,Kate

McCallum Email Blockstructure

Four most prominent topicsin discussions with ____?

Two most prominent topicsin discussions with ____?

Words Problove 0.030514house 0.015402

0.013659time 0.012351great 0.011334hope 0.011043dinner 0.00959saturday 0.009154left 0.009154ll 0.009009

0.008282visit 0.008137evening 0.008137stay 0.007847bring 0.007701weekend 0.007411road 0.00712sunday 0.006829kids 0.006539flight 0.006539

Words Probtoday 0.051152tomorrow 0.045393time 0.041289ll 0.039145meeting 0.033877week 0.025484talk 0.024626meet 0.023279morning 0.022789monday 0.020767back 0.019358call 0.016418free 0.015621home 0.013967won 0.013783day 0.01311hope 0.012987leave 0.012987office 0.012742tuesday 0.012558

Topic 37Words Prob Sender Recipient Probjean 0.032349 jean mccallum 0.111932james 0.02684 mccallum jean 0.072785lab 0.017229 jean jean 0.056024space 0.016292 mccallum allan 0.054266ciir 0.015471 jean allan 0.035162students 0.015354 allan mccallum 0.027778bruce 0.014885 mccallum croft 0.025668office 0.014768 mccallum grupen 0.021917staff 0.013244 mccallum stowell 0.01887funding 0.012072 mccallum [email protected] 0.011017 grupen mccallum 0.018167adam 0.010666 gwking mccallum 0.016057move 0.010666 [email protected] 0.014299grad 0.01008 mccallum saunders 0.014299ll 0.009962 mccallum jensen 0.007267rod 0.009494 mccallum culotta 0.006915time 0.008556 gauthier mccallum 0.006564asked 0.008556 grupen grupen 0.006446student 0.008556 corrada mccallum 0.006212today 0.008322 mccallum pal 0.005626

Topic 40

Words Prob Sender Recipient Probcode 0.060565 hough mccallum 0.067076mallet 0.042015 mccallum hough 0.041032files 0.029115 mikem mccallum 0.028501al 0.024201 culotta mccallum 0.026044file 0.023587 saunders mccallum 0.021376version 0.022113 mccallum saunders 0.019656java 0.021499 pereira mccallum 0.017813test 0.020025 casutton mccallum 0.017199problem 0.018305 mccallum ronb 0.013514run 0.015356 mccallum pereira 0.013145cvs 0.013391 hough melinda.gervasio 0.013022add 0.012776 mccallum casutton 0.013022directory 0.012408 fuchun mccallum 0.010811release 0.012285 mccallum culotta 0.009828output 0.011916 ronb mccallum 0.009705bug 0.011179 westy hough 0.009214source 0.010197 xuerui corrada 0.0086ps 0.009705 ghuang mccallum 0.008354log 0.008968 khash mccallum 0.008231created 0.0086 melinda.gervasiomccallum 0.008108

Pairs with highestrank difference between ART & SNA

5 other professors3 other ML researchers

Role-Author-Recipient-Topic Models

Main Application Project:


ResearchPaper

Cites


ResearchPaper

Cites

Person

UniversityVenue

Grant

Groups

Expertise

Summary

• Conditional Random Fields combine the benefits of– Conditional probability models (arbitrary features)– Markov models (for sequences or other relations)

• Success in– Factorial finite state models– Jointly labeling distant entities– Coreference analysis– Segmentation uncertainty aiding coreference

• Current projects– Email, contact management, expert-finding, SNA– Mining the scientific literature

End of Talk