TY  - RPRT
A1  - Stuckardt, Roland
T1  - A machine learning approach to preference strategies for anaphor resolution
N2  - 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.
KW  - Textanalyse ; Linguistische Datenverarbeitung; Computerlinguistik
Y1  - 2005
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4188
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-12982
UR  - http://www.stuckardt.de
N1  - auch in: António Branco, Tony McEnery, Ruslan Mitkov (Hrsg.), /Anaphora Processing: Linguistic, Cognitive, and Computational Modelling./ John Benjamins, Januar 2005, S. 47-72
ER  -