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 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.
Verfasserangaben: | Roland StuckardtGND |
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URN: | urn:nbn:de:hebis:30-12948 |
Titel des übergeordneten Werkes (Englisch): | Proc. 4th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC2002), University of Lisbon, Sept. 2002, 211-216 |
Dokumentart: | Konferenzveröffentlichung |
Sprache: | Englisch |
Jahr der Fertigstellung: | 2002 |
Jahr der Erstveröffentlichung: | 2002 |
Veröffentlichende Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Datum der Freischaltung: | 26.07.2005 |
GND-Schlagwort: | Textanalyse; Linguistische Datenverarbeitung; Computerlinguistik |
Seitenzahl: | 6 |
Quelle: | Publ. in: Proc. 4th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC2002), University of Lisbon, Sept. 2002, 211-216 |
HeBIS-PPN: | 226532844 |
Institute: | Informatik und Mathematik / Informatik |
DDC-Klassifikation: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Lizenz (Deutsch): | Deutsches Urheberrecht |