LREC 2006 - Proceedings sorted by papers

Title	Coreference Resolution with and without Linguistic Knowledge
Authors	O. Uryupina
Abstract	State-of-the-art statistical approaches to the Coreference Resolution task rely on sophisticated modeling, but very few (10-20) simple features. In this paper we propose to extend the standard feature set substantially, incorporating more linguistic knowledge. To investigate the usability of linguistically motivated features, we evaluate our system for a variety of machine learners on the standard dataset (MUC-7) with the traditional learning set-up.
Keywords	Anaphora, Coreference
Full paper	Coreference Resolution with and without Linguistic Knowledge

Title

Coreference Resolution with and without Linguistic Knowledge

Authors

O. Uryupina

Abstract

State-of-the-art statistical approaches to the Coreference Resolution task rely on sophisticated modeling, but very few (10-20) simple features. In this paper we propose to extend the standard feature set substantially, incorporating more linguistic knowledge. To investigate the usability of linguistically motivated features, we evaluate our system for a variety of machine learners on the standard dataset (MUC-7) with the traditional learning set-up.

Keywords

Anaphora, Coreference

Full paper

Coreference Resolution with and without Linguistic Knowledge