{"type":"ScholarlyArticle","title":"A Ranking Approach to Pronoun Resolution","authors":[{"name":"Pascal Denis","slug":"pascal-denis","given":"Pascal","family":"Denis","affiliation":""},{"name":"Jason Baldridge","slug":"jason-baldridge","given":"Jason","family":"Baldridge","affiliation":""}],"abstract":"We propose a supervised maximum entropy ranking approach to pronoun resolution as an alternative to commonly used classification-based approaches. Classification approaches consider only one or two candidate antecedents for a pronoun at a time, whereas ranking allows all candidates to be evaluated together. We argue that this provides a more natural fit for the task than classification and show that it delivers significant performance improvements on the ACE datasets. In particular, our ranker obtains an error reduction of 9.7% over the best classification approach, the twin-candidate model. Furthermore, we show that the ranker offers some computational advantage over the twin-candidate classifier, since it easily allows the inclusion of more candidate antecedents during training. This approach leads to a further error reduction of 5.4% (a total reduction of 14.6% over the twin-candidate model).","doi":null,"url":"https://ijcai.org/proceedings/2007/256","pdf":"https://ijcai.org/proceedings/2007/0256.pdf","markdown":"https://ijcai.org/proceedings/2007/256.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/256","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":256,"pages":{"first":1588,"last":1592},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}