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Relation Extraction  and  Machine Learning for IE Feiyu Xu feiyu@dfki.de  Language Technology-Lab  DFKI, Saarbrücken
Relation in IE
On the Notion  Relation Extraction ,[object Object]
Types of Information Extraction in LT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of Information Extraction in LT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Types of Relation Extraction
Information Extraction: A Pragmatic Approach ,[object Object],[object Object],[object Object],[Appelt, 2003]
Message Understanding Conferences [MUC-7 98] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation of IE systems in MUC ,[object Object],[object Object],[object Object]
Evaluation of IE systems in MUC ,[object Object],[object Object]
Generic IE tasks for MUC-7   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Generic IE tasks for MUC-7   ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Generic IE tasks for MUC-7
[object Object],[object Object],[object Object],Generic IE tasks for MUC-7
Tasks evaluated in MUC 3-7 [Chinchor, 98] YES YES YES YES YES MUC-7 YES YES YES YES MUC-6 YES MUC-5 YES MUC-4 YES MUC-3 ST TR RE CO NE EVALASK
Maximum Results Reported in MUC-7   65 86 87 69 95 PRECISION 42 67 86 56 92 RECALL ST TR TE CO NE MEASSUREASK
MUC and Scenario Templates ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Appelt, 2003]
Problems with Scenario Template ,[object Object],[object Object],[Appelt, 2003]
Addressing the Problem ,[object Object],[object Object],[Appelt, 2003]
The ACE Program ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Appelt, 2003]
Components of a Semantic Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Components of a Semantic Model ,[object Object],[object Object],[object Object]
Relations in Time ,[object Object],[object Object],[object Object],[object Object]
Relations vs. Features or Roles in AVMs ,[object Object],[object Object],name: Condoleezza Rice office: National Security Advisor age: 49 gender: female
Semantic Analysis: Relating Language to the Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Appelt, 2003]
Language and World Model Linguistic Mention Linguistic Entity Denotes Denotes [Appelt, 2003]
NLP Tasks in an Extraction System [Appelt, 2003] Cross-Document Coreference Linguistic Mention Recognition Type Classification Linguistic Entity Coreference Events and Relations Event Recognition
The Basic Semantic Tasks of an IE System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Appelt, 2003]
The ACE Ontology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Appelt, 2003]
Why GPEs ,[object Object],[object Object],[object Object]
Aspects of GPEs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of Linguistic Mentions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Entity and Mention Example [COLOGNE, [Germany]]  ( AP ) _  [A   [Chilean]   exile]  has filed a complaint against  [former   [Chilean]   dictator Gen. Augusto Pinochet]  accusing  [him] of responsibility for  [her]  arrest and torture in  [Chile]  in 1973, [prosecutors]  said Tuesday. [The woman ,  [[a Chilean]   who has since gained  [German]  citizenship]] , accused  [Pinochet]  of depriving  [her]  of personal liberty and causing bodily harm during  [her]  arrest and torture. Person Organization Geopolitical Entity
Explicit and Implicit Relations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Another Example ,[object Object],[object Object],[object Object]
Explicit Relations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of ACE Relations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Event Types (preliminary) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning  for  Relation Extraction
Motivations of ML ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problems ,[object Object],[object Object],[object Object],[object Object]
Parameters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning Methods for Template Filling Rules ,[object Object],[object Object],[object Object],[object Object]
Documents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Information Extraction” From Free Text 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 Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation NAME  TITLE  ORGANIZATION Bill Gates CEO Microsoft Bill  Veghte VP Microsoft Richard  Stallman founder Free Soft.. * * * *
IE from Research Papers
Extracting Job Openings from the Web : Semi -Structured Data   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
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Free Text
NLP-based Supervised Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AutoSlog (1993) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Workflow conceptual sentence parser (CIRUS) rule learner template filling  Rule slot filler: Target: „public building“ ...,  public buildings  were bombed and  a car-bomb was detonated slot fillers (answer keys) linguistic  patterns <subject > passive-verb documents
Linguistic Patterns
 
Error Sources ,[object Object],[object Object],[object Object]
Training Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
NLP-based ML Approaches ,[object Object],[object Object],[object Object],[object Object]
LIEP [1995] The Parliament building  was bombed by  Carlos .
PALKA [1995] The Parliament building  was bombed by  Carlos .
HASTEN [1995] The Parliament building  was bombed by  Carlos . ,[object Object],[object Object]
CRYSTAL [1995] The Parliament building  was bombed by  Carlos .
A Few Remarks ,[object Object],[object Object],[object Object]
Semi-Supervised Approaches
AutoSlog TS  [Riloff, 1996] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Linguistic Patterns
 
Pattern Extraction ,[object Object],[object Object]
Relevance Filtering ,[object Object],[object Object],[object Object]
Statistical Filtering Relevance Rate:     rel - freq i Pr(relevant text text contains case frame i  ) =    total - freq i rel-freq i :  number of instances of  case - frame i   in the relevant documents total-freq i :  total number of instances of  case - frame i Ranking Function: score i  = relevance rate i  * log 2  (frequency i  ) Pr < 0,5   negatively correlated with the domain
„ Top“
E mpirical  R esults ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unsupervised
ExDisco (Yangarber 2001) ,[object Object],[object Object],[object Object]
Input ,[object Object],[object Object],[object Object]
NLP as Preprocessing ,[object Object],[object Object],[object Object]
Duality/Density Principle (boostrapping) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Workflow documents seeds ExDisco P partition/classifier pattern  extraction filtering Dependency  Parser Named Entity Recognition relevant documents irrelevant documents new seeds
Pattern Ranking ,[object Object],|H| LOG (|H  R|) .
Evaluation of Event Extraction
ExDisco ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Relational learning and  Inductive Logic Programming (ILP) ,[object Object]
Semi-Structured and Un-Structured Documents
RAPIER [Califf, 1998] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RAPIER [Califf, 1998] ,[object Object],[object Object],[object Object],[object Object]
Filled template of RAPIER
RAPIER’s rule representation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Pattern ,[object Object],[object Object],[object Object],[object Object],[object Object]
RAPIER Rule
RAPIER’S Learning Algorithm ,[object Object],[object Object],[object Object]
Implementation ,[object Object],[object Object],[object Object],[object Object]
Example Located in Atlanta, Georgia. Offices in Kansas City, Missouri
Example (cont)
Example (cont) Final best rule:
Experimental Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Performance on job postings
Results for seminar announcement task
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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  • 1. Relation Extraction and Machine Learning for IE Feiyu Xu feiyu@dfki.de Language Technology-Lab DFKI, Saarbrücken
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  • 14. Tasks evaluated in MUC 3-7 [Chinchor, 98] YES YES YES YES YES MUC-7 YES YES YES YES MUC-6 YES MUC-5 YES MUC-4 YES MUC-3 ST TR RE CO NE EVALASK
  • 15. Maximum Results Reported in MUC-7 65 86 87 69 95 PRECISION 42 67 86 56 92 RECALL ST TR TE CO NE MEASSUREASK
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  • 25. Language and World Model Linguistic Mention Linguistic Entity Denotes Denotes [Appelt, 2003]
  • 26. NLP Tasks in an Extraction System [Appelt, 2003] Cross-Document Coreference Linguistic Mention Recognition Type Classification Linguistic Entity Coreference Events and Relations Event Recognition
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  • 32. Entity and Mention Example [COLOGNE, [Germany]] ( AP ) _ [A [Chilean] exile] has filed a complaint against [former [Chilean] dictator Gen. Augusto Pinochet] accusing [him] of responsibility for [her] arrest and torture in [Chile] in 1973, [prosecutors] said Tuesday. [The woman , [[a Chilean] who has since gained [German] citizenship]] , accused [Pinochet] of depriving [her] of personal liberty and causing bodily harm during [her] arrest and torture. Person Organization Geopolitical Entity
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  • 38. Machine Learning for Relation Extraction
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  • 44. “ Information Extraction” From Free Text 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 &quot;cancer&quot; that stifled technological innovation. Today, Microsoft claims to &quot;love&quot; 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. &quot;We can be open source. We love the concept of shared source,&quot; said Bill Veghte , a Microsoft VP . &quot;That's a super-important shift for us in terms of code access.“ Richard Stallman , founder of the Free Software Foundation , countered saying… Microsoft Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation NAME TITLE ORGANIZATION Bill Gates CEO Microsoft Bill Veghte VP Microsoft Richard Stallman founder Free Soft.. * * * *
  • 46. Extracting Job Openings from the Web : Semi -Structured Data 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
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  • 49.
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  • 51. Workflow conceptual sentence parser (CIRUS) rule learner template filling Rule slot filler: Target: „public building“ ..., public buildings were bombed and a car-bomb was detonated slot fillers (answer keys) linguistic patterns <subject > passive-verb documents
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  • 59. LIEP [1995] The Parliament building was bombed by Carlos .
  • 60. PALKA [1995] The Parliament building was bombed by Carlos .
  • 61.
  • 62. CRYSTAL [1995] The Parliament building was bombed by Carlos .
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  • 70. Statistical Filtering Relevance Rate: rel - freq i Pr(relevant text text contains case frame i ) = total - freq i rel-freq i : number of instances of case - frame i in the relevant documents total-freq i : total number of instances of case - frame i Ranking Function: score i = relevance rate i * log 2 (frequency i ) Pr < 0,5 negatively correlated with the domain
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  • 78.
  • 79.
  • 80. Workflow documents seeds ExDisco P partition/classifier pattern extraction filtering Dependency Parser Named Entity Recognition relevant documents irrelevant documents new seeds
  • 81.
  • 82. Evaluation of Event Extraction
  • 83.
  • 84.
  • 86.
  • 87.
  • 89.
  • 90.
  • 92.
  • 93.
  • 94. Example Located in Atlanta, Georgia. Offices in Kansas City, Missouri
  • 96. Example (cont) Final best rule:
  • 97.
  • 98. Performance on job postings
  • 99. Results for seminar announcement task
  • 100.
  • 101.

Editor's Notes

  1. CiteSeer, which we all know and love, is made possible by the automatic extraction of title and author information from research paper headers and references in order to build the reference graph that we use to find related work.
  2. WB’s first application area is extracting job openings from the web. Start at company home page, automatically follow links to find the job openings section, segment job openings from each other, and extract job title, location, description, and all of these fields. (Notice that capitalization, position on page, bold face are just as much indicators of this being a job title as the words themselves. Notice also that part of the IE task is figuring out which links to follow. And often data for a relation came from multiple different pages.)
  3. RL, by definition, require data in some relational format.