Automatic Acquisition of Lexical Classes and Extraction Patterns for Information Extraction

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Automatic Acquisition ofLexical Classes and
Extraction Patternsfor Information Extraction

• Kiyoshi Sudo
• Ph.D. Research Proposal
• New York University

Committee Ralph Grishman Satoshi Sekine I. Dan
Melamed
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Outline

• Introduction
• Research Proposal
• Problem Setting
• Approach
• Application to Information Extraction
• Discussion

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MUC Scenario Template Task

• MURREE, Pakistan (AP) -- Masked gunmen firing
  Kalashnikov rifles burst through the front gates
  of a Christian school Monday, killing six people
  and wounding three in the latest attack against
  Western interests since Pakistan joined the war
  against terrorism.

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MUC Scenario Template Task

• MURREE, Pakistan (AP) -- Masked gunmen firing
  Kalashnikov rifles burst through the front gates
  of a Christian school Monday, killing six people
  and wounding three in the latest attack against
  Western interests since Pakistan joined the war
  against terrorism.

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High Cost forAcquiring Knowledge-Base

• Find extraction patterns
• Find relevant documents
• Find relevant events
• Analyze sentences
• Find domain-specific lexicon
• Find existing KB (e.g. thesaurus, gazetteers)

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Prior Work
Automatic Knowledge Acquisition
Lexical Acquisition
Pattern Acquisition
Mutual Bootstrapping (Riloff and Jones 1999)
Pattern Discovery with Document
Re-ranking (Yangarber et al. 2000)
Simultaneous Multi-Semantic Class (Thelen and
Riloff 2002)
(Yangarber et al. 2002)
Pattern Acquisition for QA (Ravichandran and
Hovy 2002)
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Challenge
User
Seed Lexicon Seed Pattern
Expanded Lexicon Expanded Pattern Set
Knowledge Base
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Meeting the Challenge
User
Seed Lexicon Seed Pattern
Expanded Lexicon Expanded Pattern Set
Knowledge Base
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Semantic Clustering

• Input

• Description specific enough
• to define the scenario

• (terrorism, bombing, kidnapping)

• Tell me about the terrorism action,
• such as bombing and kidnapping.

• Goal

• Find Scenario-specific Semantic Clusters
• each of which consists of

• Semantic Lexicon

• Extraction Patterns

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Benefit for User

• Simplify Domain Analysis

• Low-cost
• Knowledge-base Acquisition
• for IE systems

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Extraction Patterns

• Definition

where
c unifies with the context that is defined by
semantic class L
Vsubj
Vobj
(cf. Sudo et al. 2001)
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Outline

• Introduction
• Research Proposal
• Problem Setting
• Approach
• Information Extraction
• Evaluation

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Overview
Semantic Clustering
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Overview
Semantic Clustering
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Information Retrieval

• Get Relevant Document set
• Get list of lexical items and extraction patterns
  ordered by relevance to the scenario
• TF/IDF scoring

R
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Example of TF/IDF scoring(Management Succession
Business)
300 documents retrieved From WSJ (7/94 - 8/94)
Extracted by MINIPAR (Lin 1998)
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Overview
Semantic Clustering
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Bootstrapping

• Find one cluster that consists of Lexicon and
  Extraction Patterns

• Assumption
• Patterns provide Lexical Classes.
• Lexicon provides contextual information.

Riloff and Jones 1999 Agichtein and Gravano 2000
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Bootstrapping (Cont.)

• Algorithm (cf. Riloff and Jones 1999)
• Given
• the ordered list of terms
• the ordered list of extraction patterns
• Lexicon (), Pattern ()
• w ? the most relevant term in the list and add it
  into Lexicon
• p ? the most relevant pattern among those that
  extract w.
• Add p into Pattern
• w ?the most relevant term among those that are
  extracted by p
• Add w into Lexicon
• Go to 1

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Example of Bootstrapping(Management Succession
Business)
From WSJ (7/94 - 8/94)
Extracted by MINIPAR (Lin 1998)
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Example of Bootstrapping(Management Succession
Business)
From WSJ (7/94 - 8/94)
Extracted by MINIPAR (Lin 1998)
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ProblemPolysemous Lexicon, Pattern

• Lexicon can be ambiguous
• e.g. Clinton (Person, Organization, Location )
• Extraction patterns can be ambiguous
• e.g. be killed in ltxgt (x Location, Date )
• Needs more study
• more restriction
• Probabilistic Model ??

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Overview
Semantic Clustering
Source
Information Retrieval
Boot- strapping
Query Expansion
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Query Expansion

• Generalize terms in a query with a newly
  discovered cluster
• cf. Rocchio 1971 (Vector model)
• Zhai and Lafferty 2001 (Language-modeling)

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Overview
Semantic Clustering
Source
Information Retrieval
Boot- strapping
Query Expansion
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Outline

• Introduction
• Research Proposal
• Problem Setting
• Approach
• Application to Information Extraction
• Discussion

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Application toInformation Extraction
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Human Intervention

• Extraction patterns
• Event pattern
• Context contains a verb or nominalization of verb
• Used for event extraction and role assignment
• e.g. (terrorist, fire, x)
• Local pattern
• Context contains only enough information to
  recognize semantic class
• Used for entity recognition only
• e.g. (x,Inc.)
• Association of Event Pattern to Role
• e.g. (company, hire, x)?PersonIn and (company,
  fire, x)?PersonOut

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Outline

• Introduction
• Research Proposal
• Problem Setting
• Approach
• Application to Information Extraction
• Discussion

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Discussion

• Domain Portability
• User only needs to specify the scenario
• Language Portability
• Language-dependent Tools
• Segmentation (Lemmatization)
• Dependency Parsing

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Evaluation

• MUC-style (Scenario-Template task)
• Slot-base
• Precision, Recall, F-measure
• Domain Portability
• Several pre-defined tasks that differ in
  difficulty
• Language Portability
• Japanese
• English

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Contribution

• Tool for Domain Analysis
• Low-cost Knowledge-base Acquisition
• Towards Open-domain Information Extraction

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Conclusion

• Proposed New Approach for Knowledge-base
  Acquisition (Semantic Clustering)
• Discussed Application of Acquired KB to
  Information Extraction (Human Intervention and
  Local vs. Event patterns)
• Discussed Evaluation with several predefined
  MUC-style tasks different in difficulty and
  across languages (Domain portability and Language
  portability)

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ToDo

• Implementation
• Preparation for Evaluation
• Evaluation

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Time for Questions(Conclusion)

• Proposed New Approach for Knowledge-base
  Acquisition (Semantic Clustering)
• Discussed Application of Acquired KB to
  Information Extraction (Human Intervention and
  Local vs. Event patterns)
• Discussed Evaluation with several predefined
  MUC-style tasks different in difficulty and
  across languages (Domain portability and Language
  portability)