Machine Learning for Information Extraction

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Machine Learning for Information Extraction

• Li Xu

2
Objective

• Learn how to apply the machine learning concept
  to the application
• Learn how to improve the performance of the
  existed application by applying the machine
  learning algorithms

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Introduction

• Information Extraction (IE) is concerned with
  extracting the relevant data from a collection of
  document.
• Key component extraction patterns.
• Machine Learning algorithms.

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IE for Free Text

• Syntactic and semantic constraints
• AutoSlog
• LIEP
• PALKA
• CRYSTAL
• CRYSTAL Webfoot
• HASTEN

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IE from online Document

• WHISK (Soderland 1998)
• Domain Rental Ads
• Precision 95 Recall 73-90
• RAPIER (Califf Mooney 1997)
• Domain software jobs
• Precision 84 Recall 53
• SRV (Freitag 1998)
• Domain Seminar announcement
• Precision Speaker, 75 Location,75 start time
  99, end time 96.

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WHISK
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RAPIER
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SRV
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Problems

• Bottom-up search
• RAPIER
• WHISK
• Single-slot extraction rules
• SRV
• RAPIER
• Heavily depend on the layout pattern

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Obituary Ontology
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Improvement
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Lexical Object

• Relational Learning
• FOIL
• Feature design
• Regular expression
• Rote Learning

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Multi-slot Hierarchy
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Multi-slot Boundary

• Relational Learning
• Feature Design
• Individual heuristics
• Combining heuristics

15
Conclusion

• How to applying the machine learning algorithm to
  IE?
• What is the problem for each system?
• How to improve an existed IE approach through
  machine learning? And how to avoid the problems
  appeared in other machine learning based IE
  systems?