@inproceedings{34ef118e86de473abd4d46af952fbba9,
title = "Confidence measures and thresholding in coreference resolution",
abstract = "Coreference resolution is an important component of information extraction systems. Machine learning methods have been found to perform quite well for this task, leading to research in a variety of such methods. In our current work, we explore the approach of increasing the performance of an existing pairwise coreference system by using a confidence measure in order to filter out low-scoring classifications. We explore several ways to define a confidence measure. Subsequently, we use a confidence measure in conjunction with thresholding. We find that a multiple threshold system, with the thresholds defined the right way, outperforms both the baseline and a single threshold system. We also discover that basing a threshold as close as possible to the evaluation metric is a good idea, and explore reasons why this might be so.",
keywords = "Confidence estimation, Coreference resolution, Machine learning",
author = "John Chen and Laurie Crist and Len Enyon and Cassandre Creswell and Amit Mhatre and Rohini Srihari",
year = "2007",
language = "English",
series = "International Conference Recent Advances in Natural Language Processing, RANLP",
publisher = "Association for Computational Linguistics (ACL)",
pages = "121--127",
editor = "Galia Angelova and Kalina Bontcheva and Ruslan Mitkov and Nicolas Nicolov and Nikolai Nikolov",
booktitle = "International Conference Recent Advances in Natural Language Processing, RANLP 2007 - Proceedings",
note = "International Conference Recent Advances in Natural Language Processing, RANLP 2007 ; Conference date: 27-09-2007 Through 29-09-2007",
}