A machine learning-based coreference detection system for OntoNotes

Authors:
Yaqin Yang;Nianwen Xue;Peter Anick
Affiliations:
Brandeis University, Waltham, Massachusetts;Brandeis University, Waltham, Massachusetts;Brandeis University, Waltham, Massachusetts
Venue:
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Year:
2011

Citing 2
Cited 1

A machine learning approach to coreference resolution of noun phrases

Computational Linguistics - Special issue on computational anaphora resolution
Unrestricted Coreference: Identifying Entities and Events in OntoNotes

ICSC '07 Proceedings of the International Conference on Semantic Computing

Incorporating rule-based and statistic-based techniques for coreference resolution

CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we describe the algorithms and experimental results of Brandeis University in the participation of the CoNLL Task 2011 closed track. We report the features used in our system, and describe a novel cluster-based chaining algorithm to improve performance of coreference identification. We evaluate the system using the OntoNotes data set and describe our results.