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Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping

Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping. Ellen Riloff and Rosie Jones, AAAI 99 Presented by: Sunandan Chakraborty. Information Extraction. Extracting domain-specific information from NL text Example Domains Locations Companies Terrorism.

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Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping

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  1. Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping Ellen Riloff and Rosie Jones, AAAI 99 Presented by: Sunandan Chakraborty

  2. Information Extraction • Extracting domain-specific information from NL text • Example Domains • Locations • Companies • Terrorism

  3. Required Lexical Resources • Semantic lexicons • Dictionary of words tagged using semantic categories • e.g. name of locations (countries, cities) • Extraction patterns • e.g. outlets in <x>, from <y> • From Noun Phrase • Outlets in New York

  4. Mutual Bootstrapping • No annotated corpus • Learning extraction patterns and semantic lexicon • Input • Unannotated corpus • Seed words

  5. Mutual Bootstrapping • Starting from seed words • Identifying NPs related to the seed words [for extraction patterns] • Using extraction patterns to identify new terms • New terms should be in the same lexical category • Using new terms to search for more patterns

  6. Algorithm • Input: • Candidate extraction pattern from AutoSlog • Seed words • Data Structures • EPdata – to store candidate extraction patterns • Initial value: extraction patterns from AutoSlog an the extractions • SemLex – to store semantic lexicons as they are identified • Initial value: seed words • Cat_EPlist – to store the extraction patterns • Initial value: null

  7. Algorithm (contd...) • For all Extraction Patterns Pi in EPdata • score(Pi) = Ri * log2(Fi) • Fi = no. of lexicons produced by Pi • Ri = Fi/Ni, Ni: no. of NPs extracted by Pi • 2. Insert Pi to Cat_Eplist, wherescore(Pi) is max • 3. Insert Pi’s extraction SemLex • 4. Repeat from step 1.

  8. Results (Locations)

  9. Multi-level Bootstrapping • Problem with mutual bootstrapping • Insertion of incorrect word in SemLex can drastically reduce accuracy • Solution • Second level of bootstrapping

  10. Meta-bootstrapping • Outer level of bootstrapping • Retains the best 5 NPs • Corresponding lexicons are added to a permanent list • Reliability score: • rel(NPi) = ΣNik=1(1+ score(pk)) • Using reliable lexicons for the next iteration of Mutual-BS

  11. Results

  12. Evaluation • Corpus • 4160 Corporate web pages • 1500 terrorism text • AutoSlog candidate extraction patterns • 19,690 for the web pages • 14,064 for the terrorism text • Seed words • Web company: Co., Company, Corp… • Web Location: Different country names • Terrorism location: Bolivia, city, Colombia, district

  13. Evaluation (contd…) • 50 iterations of Meta-bootstrapping • Mutual bootstrapping ran until to produced 10 unique patterns

  14. Evaluation (contd…) • Other systems’ accuracy (weapon): • 17% (Rilof & Shepherd, 1997) • 36% (Roark & Charniak, 1998)

  15. Evaluation (contd…) • Tested on 233 new web pages

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