Machine-learning-based vs. manually designed approaches to anaphor resolution: the best of two worlds

In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line 
In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line of research, corpus-based approaches have been investigated that employ machine-learning techniques for deriving strategies automatically. Since the knowledge-engineering effort for designing and optimizing the strategies is reduced, the latter approaches are considered particularly attractive. Since, however, the hand-coding of robust antecedent filtering strategies such as syntactic disjoint reference and agreement in person, number, and gender constitutes a once-for-all effort, the question arises whether at all they should be derived automatically. In this paper, it is investigated what might be gained by combining the best of two worlds: designing the universally valid antecedent filtering strategies manually, in a once-for-all fashion, and deriving the (potentially genre-specific) antecedent selection strategies automatically by applying machine-learning techniques. An anaphor resolution system ROSANA-ML, which follows this paradigm, is designed and implemented. Through a series of formal evaluations, it is shown that, while exhibiting additional advantages, ROSANAML reaches a performance level that compares with the performance of its manually designed ancestor ROSANA.
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Metadaten
Author:Roland Stuckardt
URN:urn:nbn:de:hebis:30-12948
Parent Title (English):Proc. 4th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC2002), University of Lisbon, Sept. 2002, 211-216
Document Type:Conference Proceeding
Language:English
Year of Completion:2002
Year of first Publication:2002
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2005/07/26
SWD-Keyword:Computerlinguistik; Linguistische Datenverarbeitung; Textanalyse
Pagenumber:6
Source:Publ. in: Proc. 4th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC2002), University of Lisbon, Sept. 2002, 211-216
HeBIS PPN:226532844
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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