A Probabilistic Genre-independent Model of Pronominalization

Michael Strube, Maria Wolters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Our aim in this paper is to identify genreindependent factors that influence the decision to pronominalize. Results based on the annotation of twelve texts from four genres show that only a few factors have a strong influence on pronominalization across genres, i.e. distance from last mention, agreement, and form of the antecedent. Finally, we describe a probabilistic model of pronominalization derived from our data.
Original languageEnglish
Title of host publicationProceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 2000
EventANLP/NAACL 2000 - Seattle, WA, United States
Duration: 4 May 20004 May 2000

Publication series

NameNAACL 2000
PublisherAssociation for Computational Linguistics


WorkshopANLP/NAACL 2000
Country/TerritoryUnited States
CitySeattle, WA


Dive into the research topics of 'A Probabilistic Genre-independent Model of Pronominalization'. Together they form a unique fingerprint.

Cite this