Article by: Feiyu Xu, Daniela Kurz, Jakub Piskorski, Sven Schmeier

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An Domain Adaptive Approach to Automatic Acquisiti
on of Domain Relevant Terms and their Relations
with Bootstrapping

• Article by Feiyu Xu, Daniela Kurz, Jakub
  Piskorski, Sven Schmeier
• Article Summary by Mark Vickers

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Presentation Layout

• Introduction to Research
• Methods used
• Overview of GermaNet
• Overview of SPPC
• Details of their Approach
• Results
• Conclusion

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Goal

• Introduction

Methods Used
GermaNet
SPPC
Approach
Results
Conclusion

• Automatic Acquisition of Domain Relevant terms
  and their relations
• How?
• Single-word Terms TFIDF classification
• Domain Relevant Relations
• Use Lexico-syntactic patters
• Existing Ontologies
• Collocation methods

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Input

• Introduction

Methods Used
GermaNet
SPPC
Approach
Results
Conclusion

• No seed words
• No syntactic patterns
• Just a collection of classified documents

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Methods Used
Introduction

• Methods Used

GermaNet
SPPC
Approach
Results
Conclusion

• Builds on Other Systems
• GermaNet
• (They built an Ontology Inference Machine
• to search GermaNet)
• For Accessing Semantic relations
• SPPC (Shallow Processing Production Center)
• For Linguistic Annotation

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Accessing Semantic RelationsGermaNet
Introduction
Methods Used

• GermaNet

SPPC
Approach
Results
Conclusion

• Developed within the LSD Project at the Division
  of Computational Linguistics of the Linguistics
  Department at the University of Tübingen, Germany
• A lexical-semantic net
• German nouns, verbs, and adjectives are
  semantically grouped by an underlying
  lexical concept (like a thesaurus) called
  synsets
• Synsets are connected by semantic relations
• Lexical relationships include synonyms,
  antonyms, and pertains to
• Conceptual relations include hyponyms (is-a),
  meronyms (has-a), entailment, and cause
• Based off the technology of WordNet (Princeton)

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Accessing Semantic Relations WordNet
Introduction
Methods Used

• GermaNet

SPPC
Approach
Results
Conclusion
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Accessing Semantic Relations WordNet
Introduction
Methods Used

• GermaNet

SPPC
Approach
Results
Conclusion
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Accessing Semantic Relations WordNet
Introduction
Methods Used

• GermaNet

SPPC
Approach
Results
Conclusion
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Accessing Semantic RelationsInference Machine
Introduction
Methods Used

• GermaNet

SPPC
Approach
Results
Conclusion

• Allows GermaNets relations to be searched by
  other applications
• Provides 3 different functions
• Retrieval of relations assigned to words
• Example Find all synonyms for the word bar ?
  rod, saloon,
• Retrieval of relations between words
• Example Find relations between
  Internet-Service-Provider and Company ? hyponym
  (so and ISP is a company)
• Navigation in the GermaNet graph

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Linguistic AnnotationSPPC
Introduction
Methods Used
GermaNet

• SPPC

Approach
Results
Conclusion

• SPPC (Shallow Processing Production Center)
• Robust German NLP that uses cascaded optimized
  weighted finite state devices
• SPPC parts
• Tokenizer
• Lexical Processor
• Part-of-Speech Filtering
• Named-entity Finder
• Chunk recognizer

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Their Extraction Engine
Introduction
Methods Used
GermaNet
SPPC

• Approach

Results
Conclusion

• Three Main components
• TFIDF-based single-word term classifier
• Lexico-syntactic pattern finder
• Learns patterns based on known relations
• Learns patterns based on term collocation methods
• Relation Extractor

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Their Extraction Engine
Introduction
Methods Used
GermaNet
SPPC

• Approach

Results
Conclusion
1. Extract Single-word terms
Single-word term extraction (KFIDF)
2. Learn multi-word terms identify syntactic
patterns
3. Learn patterns from known relations
4. Extract related terms using found
lexico-syntactic patterns
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Discovering Domain Relevant Terms
Introduction
Methods Used
GermaNet
SPPC

• Approach

Results
Conclusion

• Apply a TFIDF measure KFIDF

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Their Extraction Engine
Introduction
Methods Used
GermaNet
SPPC

• Approach

Results
Conclusion
Collocation learner
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Learning Term Collocations
Introduction
Methods Used
GermaNet
SPPC

• Approach

Results
Conclusion

• Examples man-eating shark, dead serious, depend
  on, blue-collard
• Measures
• Mutual Information (probabilities)
• Occurrence of one word predicts the occurrence of
  another
• Not practical for sparse data
• Log-Likelihood Measures (contingency tables)
• - Tells how much more likely the occurrence of
  one pair is over the another
• T-test
• - Accept or reject the null hypothesis (terms are
  independent)

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Their Extraction Engine
Introduction
Methods Used
GermaNet
SPPC

• Approach

Results
Conclusion
Relation Extractor
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Introduction
Methods Used
Learning Relations withLexico-syntactic patterns
GermaNet
SPPC

• Approach

Results
Conclusion
Example of a lexico-syntactic pattern finding
relations Pattern or other Sentence Bruises,
wounds, or other injuries are common. Hyponym
Relations (Bruises, Injuries), (Wounds,
Injuries) ---------------------------------------
---------------------------------------- Pattern
as well as Sentence Cocaine as well as
Hashish, and LSD Near synonyms? -- Now we can
match LSD to Drug domain
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Introduction
Methods Used
Learning Relations withLexico-syntactic patterns
GermaNet
SPPC

• Approach

Results
Conclusion
Term relation extractor applies newly extracted
lecixo-syntactic patterns
Extracted terms
List of related terms with possible hyponymous
relations
GermaNet (semantic relationships)
Domain independent patterns
Domain specific patterns
With Near Synonyms search GermaNet to find
common hyponyms, then assign the newly found
hyponymous relation to the term not encode in the
GermaNet
Terms with semantic relations (synonymy,
hyponymy, meronymy)
Put semantically similar fragments Into
Landau-Finkelstien and Morins Algorithm to
cluster patterns
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Introduction
Methods Used
Results
GermaNet
SPPC
Approach

• Results

Conclusion

• Theres a correlation between corpus size and
  precision
• LogLike delivers best result compared to Mutual
  Information And T-Test
• Noun-Verb collocations were most prominent and
  had best results
• In Drug domain, N-V 56 precision and N-N
  41 precision

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Introduction
Methods Used
Conclusion
GermaNet
SPPC
Approach
Results

• Conclusion

• KFIDF proves promising for single-word term
  extraction
• Statistical measures are suitable for free-word
  order languages like German
• Extracting term relations useful for real-world
  IE

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Introduction
Methods Used
My Evaluation
GermaNet
SPPC
Approach
Results

• Conclusion

• Uses well known existing systems
• Seemingly no human interaction
• Domain Adaptive (robust)
• - Precision does not seem to be too impressive,
  and recall? Id like to see more results
• We see from the past few papers that automatic
  ontology generation approaches consist of
• Combining multiple strategies (statistics,
  existing ontologies)
• Have a cyclic, machine learning nature.