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Automatic Bibliographic Extraction System ABES

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Automatic Bibliographic Extraction System ABES

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    1. Automatic Bibliographic Extraction System (ABES) NLP Term Project 8 December 2005 Nick Gorski

    3. ABES algorithm First pass names consisting of NNPs Name extraction Name resolution Information extraction Entry Second pass names represented by pronouns Update possible antecedents Pronoun extraction Pronoun/antecedent resolution Information extraction and entry

    4. Name extraction and resolution

    5. Information extraction

    6. Pronoun/Antecedent resolution

    7. Pronoun/Antecedent resolution

    8. Simple, contrived examples

    9. New York Times article

    11. Is ABES perfect? Of course not! Tagging: ABES relies on the Charniak parser for tagging. When the parser makes a mistake it causes ABES to make (sometimes humorous) mistakes as well. Pronoun/Antecedent resolution: While newspapers mostly rely on simple, predictable sentence constructions, other domains dont and sometimes newspapers can trick ABES, too. Missed facts and actions: ABES sometimes misses useful facts (e.g. appositions that arent NPs) and actions (e.g. VPs following appositions).

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