Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2011

Abstract

This paper presents our participation in the CoNLL-2011 shared task, Modeling Unrestricted Coreference in OntoNotes. Coreference resolution, as a difficult and challenging problem in NLP, has attracted a lot of attention in the research community for a long time. Its objective is to determine whether two mentions in a piece of text refer to the same entity. In our system, we implement mention detection and coreference resolution seperately. For mention detection, a simple classification based method combined with several effective features is developed. For coreference resolution, we propose a link type based pre-cluster pair model. In this model, pre-clustering of all the mentions in a single document is first performed. Then for different link types, different classification models are trained to determine wheter two pre-clusters refer to the same entity. The final clustering results are generated by closest-first clustering method. Official test results for closed track reveal that our method gives a MUC F-score of 59.95%, a B-cubed F-score of 63.23%, and a CEAF F-score of 35.96% on development dataset. When using gold standard mention boundaries, we achieve MUC F-score of 55.48%, B-cubed F-score of 61.29%, and CEAF F-score of 32.53%.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 15th Conference on Computational Natural Language Learning (CoNLL 2011): Shared Task, Portland, OR, June 23-24

First Page

131

Last Page

135

ISBN

9781937284084

Publisher

Association for Computational Linguistics

City or Country

Stroudsburg, PA

Additional URL

https://aclanthology.org/W11-1922/

Download

Share

COinS